POPULATION GENETICS, FORAGING ECOLOGY, AND

TROPHIC RELATIONSHIPS OF GREY WOLVES IN

CENTRAL SASKATCHEWAN

A Thesis Submitted to

the College of Graduate Studies and Research

in Partial Fulfillment of the Requirements for

the Degree of Master of Science

in the Department of Biology

University of Saskatchewan

Saskatoon

By

Erin Jaime Moira Urton

© Copyright Erin Jaime Moira Urton, December 2004. All rights reserved.

PERMISSION TO USE

In presenting this thesis in partial fulfillment of the requirements for a postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying, publication, or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis.

Requests for permission to copy or to make other use of material in this thesis in whole or part should be addressed to: Head of the Department of Biology University of Saskatchewan Saskatoon, Saskatchewan S7N 5E2

I

ABSTRACT Habitat fragmentation and anthropogenic development influence the level of

isolation and security in and around protected habitats affecting wolf movements and the

distribution and abundance of their prey. In light of recent concern about the ecology of

animals in protected areas, I initiated a research project to investigate the molecular and

foraging ecology of grey wolves in and around Prince Albert National Park (PANP),

Saskatchewan.

Estimates of genetic diversity and population structure can be used as surrogates

to detect effects of habitat degradation on wolves. Genetic diversity was high in these

populations relative to other North American wolf populations. My results suggest that

wolves in central Saskatchewan form a panmictic population, however there is some

evidence showing partial isolation of one group of wolves within PANP. I speculate that

the level of human activity such as road networks, hunting, and trapping act as dispersal

impediments to this isolated group. Further, the genetic homogenization, indicating high

population turnover, of wolf groups that use the periphery and adjacent areas of PANP

may also contribute to the observed genetic subdivision. The partially isolated NW

group, characterized by slightly lower diversity indices, low migration rates, and higher

levels of allele fixation, indicated this group was a more stable social unit comprised of

more related individuals.

Knowledge of wolf food habits and how they change over time is a fundamental

component to understanding wolf ecology. Using scat analysis I evaluated wolf foraging

ecology by calculating indices of occurrence/faeces (OF) and percent prey biomass

contribution: white tailed deer contributed 43% and 33% respectively to wolf diet; elk

(33%, 50%), moose (7%, 14%), beaver (5%, 2%), and snowshoe hare (2%, <1%). I

found no evidence of livestock depredation nor did wolves prey on bison or caribou.

There were no differences in OF indices between years. Prey selectivity was apparent in

both years with wolves selecting elk and avoiding beaver. A diversity of ungulate prey

are readily available to wolves in this system; however, scat analysis and tests for prey

selection indicate a preference for elk. I presume this is a choice made to balance risk

with profitability of food items in concordance with optimal foraging theory.

II

I examined trophic relationships between the grey wolf and 18 mammalian

species from the boreal forest of central Saskatchewan, Canada, using δ13C and δ15N

stable isotope values measured in hair samples. Variance in isotope values for wolves and

other carnivores was investigated as a proxy for dietary variation. IsoSource, an isotopic

source partitioning model, quantified the relative proportions of 5 most likely prey items

in the diets of wolves. I compared these results with investigations of faecal contents

using percent biomass contributions of prey items in wolf diet. I found no difference

between percent biomass measures and mean percent contributions derived from

IsoSource. Despite social foraging, my results indicate highly variable diets among

individual wolves and I discuss this in terms of boreal wolf ecology.

III

ACKNOWLEDGEMENTS Completing this thesis would not have been possible without the commitment and

support of many people. I first thank my supervisors and mentors, Drs. Francois Messier

and Paul Paquet, for their patience and guidance over the duration of this project. I am

gracious for your belief in my abilities and assistance with initiating this project. I thank

Dr. Keith Hobson for many thought provoking conversations, a positive outlook, and

endless wisdom and support. I have the utmost respect and admiration for you all.

I thank my lab-mates, Yeen Ten Hwang, Justin Pitt and Naomi Carriere for their

support, stimulating conversation, and constructive criticism. Yeen Ten, you are an

amazing teacher and I wouldn’t have made it through without you (and Roxie, too!).

Chris Darimont inspired, encouraged, and advised me in many ways—you are a brilliant

friend and teacher. Several other friends and colleagues also provided enormous amounts

of shared knowledge and advice: F. Bennett, J. Grant, S. Haszard, S. Carnegie, G.

Pflueger, A. Henderson, M. Percy, M. Musiani, Y. Plante, P. Bennett, B. Bateman, T.

Sallows, and A. Vik Stronen; you were all pillars of support through this whole process.

Marco and Yves, you were life savers during my final analyses and I hope to remain

research collaborators, and more importantly friends, for a long time to come.

I owe my deepest thanks to my parents, Monty and Moira, for encouraging

everything I do. Your love and support have been essential in this life. My dreams would

never have come true without you both. Thanks for the loan, Mom-- I promise I will pay

it back soon!

Having said that, a graduate education and wolf research projects are expensive

endeavors. There are many people, agencies, and organizations that provided myself and

this project with financial and logistical support: Canadian Wildlife Service, Parks

Canada, PANP, Riding Mountain National Park, University of Saskatchewan, Mountain

Equipment Co-op Environment Fund, Saskatchewan Environment, Nature Saskatchewan,

Canadian Wildlife Foundation, Saskatchewan Wildlife Federation, Malcolm Ramsay

Memorial Scholarship, David Pattinson Memorial Scholarship, Orville Erickson

Memorial Scholarship, and the Central Rockies Wolf Project all were magnificent

supporters of my research.

IV

Many central Saskatchewan fur trappers generously contributed hide samples;

special thanks to H. Giroux, K. Barlow and K. McDaid. Patricia Healy prepared

numerous isotope samples when I was too frazzled to do them myself. S. Van

Wilgenburg, G. Rutten, and D. Weider provided fantastic GIS support during the latter

stages of the project. Many people contributed to field and lab data collection; for this I

thank: G. Pflueger (a.k.a. field assistant extraordinaire), P. Bennett, E. Tosoni, L. Green,

M. Urton, PANP Wardens, Y. Plante, B. Mann, and A. Arndt. Special thanks to S.

Cherry for doing the “crappiest” job ever and doing it well (you know what I mean!).

Delta Waterfowl Research Station, MB graciously provided the facilities for scat

analyses.

I extend my deepest appreciation to those who have given me opportunities to

work on wolf projects in the past; you are all great mentors and teachers: P. Paquet, C.

Callaghan, M. Percy, M. Hebblewhite, and T. Hurd. Thanks to D. Frandsen, N. Stolle,

and S. Woodley for supporting the initiation and perpetuation of this project and to

everyone in PANP for their interest and enthusiasm throughout.

Last but not least, enormous thanks to Linus for keeping me distracted and sane,

and for reminding me about what’s really important in life. You are the perfect de-

stressing companion; truly a girl’s best friend.

V

DEDICATION

This thesis is dedicated to the memory of Dr. Malcolm A. Ramsay. His curious mind,

philosophical nature, and devotion to ecology ignited in me a passion for investigating

the natural world.

VI

TABLE OF CONTENTS

PERMISSION TO USE.....................................................................................................I

ABSTRACT...................................................................................................................... II

ACKNOWLEDGEMENTS ........................................................................................... IV

DEDICATION.................................................................................................................VI

TABLE OF CONTENTS ............................................................................................. VII

LIST OF TABLES .......................................................................................................... IX

LIST OF FIGURES ......................................................................................................... X

1.0 GENERAL INTRODUCTION.................................................................................. 1 1.1 WOLF ECOLOGY IN FRAGMENTED HABITATS........................................................ 1 1.2 DISPERSAL AND INTER-POPULATION MOVEMENT ................................................. 2 1.3 INFLUENCE OF FORAGING ECOLOGY ON WOLF POPULATION STRUCTURE........... 3 1.4 STUDY OBJECTIVES ................................................................................................ 4 1.5 CONSERVATION NEED ............................................................................................ 4 1.6 REFERENCES .......................................................................................................... 6

2.0 GENETIC DIVERSITY AND DIFFERENTIATION OF GREY WOLF POPULATIONS IN CENTRAL SASKATCHEWAN ................................................ 11

2.1 INTRODUCTION..................................................................................................... 11 2.2 METHODS ............................................................................................................. 13

2.2.1 Study area.................................................................................................... 13 2.2.2 Non-invasive research................................................................................. 13 2.2.3 Genetic samples .......................................................................................... 15 2.2.4 DNA extraction........................................................................................... 15 2.2.5 Identification of microsatellite alleles......................................................... 17 2.2.6 Microsatellite diversity, population relationships and gene flow ............... 17

2.3 RESULTS ............................................................................................................... 19 2.3.1 DNA extraction........................................................................................... 19 2.3.2 Microsatellite diversity ............................................................................... 19 2.3.3 Population structure and gene flow............................................................. 20

2.4 DISCUSSION .......................................................................................................... 20 2.5 REFERENCES ........................................................................................................ 27

3.0 FORAGING ECOLOGY AND PREY SELECTION BY GREY WOLVES...... 33 3.1 INTRODUCTION..................................................................................................... 33 3.2 METHODS ............................................................................................................. 35

3.2.1 Study area..................................................................................................... 35 3.2.2 Field and laboratory procedures................................................................... 35

3.3 DATA ANALYSIS .................................................................................................... 38 3.3.1 Food habits.................................................................................................. 38 3.3.2 Biomass consumption ................................................................................. 38 3.3.3 Estimation of prey selectivity ..................................................................... 39

VII

3.4 RESULTS ............................................................................................................... 40 3.4.1 Wolf food habits .......................................................................................... 40 3.4.2 Biomass contribution ................................................................................... 41 3.4.3 Prey selectivity............................................................................................. 41

3.5 DISCUSSION ...................................................................................................... 41

3.5.1 Wolf diet composition.................................................................................. 41 3.5.2 Risk-avoidance and optimal foraging ......................................................... 46

3.6 REFERENCES ........................................................................................................ 49

4. 0 GREY WOLF TROPHIC RELATIONSHIPS IN THE BOREAL FOREST: INSIGHTS FROM STABLE ISOTOPES .................................................................... 55

4.1 INTRODUCTION .................................................................................................... 55 4.2 METHODS............................................................................................................. 57

4.2.1 Study area.................................................................................................... 57 4.2.2 Sample collection........................................................................................ 58 4.2.3 Isotopic analyses ......................................................................................... 58 4.2.4 Isotopic calculations.................................................................................... 59 4.2.5 IsoSource..................................................................................................... 59

4.3 RESULTS............................................................................................................... 59 4.3.1 Mammalian trophic relationships ............................................................... 59 4.3.2 IsoSource..................................................................................................... 60

4.4 DISCUSSION .......................................................................................................... 65 4.4.1 Trophic relationships involving wolves...................................................... 65 4.4.2 Individual variation..................................................................................... 67 4.4.3 Dietary mixing models................................................................................ 70

4.5 REFERENCES ........................................................................................................ 72

5.0 GENERAL DISCUSSION ....................................................................................... 79 5.1 SYNTHESIS ............................................................................................................ 79 5.2 CONSERVATION AND MANAGEMENT IMPLICATIONS ........................................... 81 5.3 CONCLUSION ........................................................................................................ 82 5.4 REFERENCES ........................................................................................................ 83

APPENDIX A. ................................................................................................................. 85

CAVEATS IN NON-INVASIVE GENETIC SAMPLING ....................................................... 85 REFERENCES ............................................................................................................... 87

VIII

LIST OF TABLES

Table 2.1: Sequences of 13 microsatellite DNA markers used in genotyping wolf Samples………………………………………………………………………….18

Table 2.2: Genetic variation at 10 microsatellite loci in central Saskatchewan wolves...21 Table 2.3: Pairwise genetic distances (lower; Nei 1978) and Fst (θ) (upper) among central

Saskatchewan wolves ……………………………………………………………21 Table 2.4: Pairwise number of migrants (Nm) among 5 gray wolf populations in central

Saskatchewan…………………………………………………………………….21 Table 3.1: Composition of wolf scats and relative biomass contribution of different prey species to winter wolf diet in Prince Albert National Park (2002, 2003)………..41 Table 3.2: Composition of wolf scats in each sampling year in Prince Albert National Park………………………………………………………………………………42 Table 4.1: Summary of means, standard deviations (SD) and ranges (min to max) for

δ15N and δ13C values in hair for 18 mammalian species sampled from the central boreal forest of Saskatchewan. ………………………………………………….61

Table 4.2: Summary of means, standard deviations (SD) and ranges (min to max) for

δ15N and δ13C values for 3 groups of wolves sampled at different locations throughout the central boreal forest of Saskatchewan…………………………...62

IX

LIST OF FIGURES Figure 2.1: Study area in the boreal forest of central Saskatchewan, Canada…………14 Figure 2.2: Wolf scat sample locations in central Saskatchewan (not all samples are

shown)………………………………………………………………………….16 Figure 2.3: Pattern of 2-dimensional spatial structuring of wolf groups represented

through Multidimensional Scaling (MDS) performed on the matrix of pairwise θ values obtained from microsatellite data……………………………………….22

Figure 2.4: Neighbor-joining tree inferred from Nei’s genetic distance measures

calculated for microsatellite data……………………………………………….23 Figure 3.1: Study area in central Saskatchewan and Prince Albert National Park…….35 Figure 3.2: Wolf scat sample locations in central Saskatchewan and PANP (not all

samples are shown)…………………………………….......................................37 Figure 3.3: Proportions of prey species in winter wolf diets in 2002 and 2003 in PANP,

central Saskatchewan……………………………………………………………43 Figure 4.1: Distribution of mean δ13C and δ15N values ± SE (‰) for 18 boreal forest

Mammals sampled in central Saskatchewan sampled over two consecutive winters (2002, 2003). Values for muskrat are not included in this plot…………60

Figure 4.2: δ13C and δ15N values and ellipses showing one standard deviation for 3

groups of wolves sampled at different locations throughout the boreal forest of central Saskatchewan, Canada…………………………………………………...63

Figure 4.3: Mixing polygon for δ13C and δ15N values of five food sources for gray wolves

in central Saskatchewan. The 25th to 75th percentile ranges for the calculated feasible distributions in IsoSource are reported………………………………….65

X

1.0 GENERAL INTRODUCTION

1.1 Wolf ecology in fragmented habitats

Few areas of the globe remain unaffected by pervasive human activity. Mosaics

of agriculture, urbanization, logged forest, and degraded land dominate much of the

landscape (Saunders et al. 1991). Alterations in the landscape have ultimately resulted in

shifts of geographic distributions of numerous species, particularly those that require

large tracts of contiguous habitat to survive. Changes in the distributions of large

carnivores, in particular, chronicle the repercussions of such landscape alterations

(Wilcox and Murphy 1985, Woodruffe 2001, With 2004). Key factors influencing the

viability of large carnivore populations are habitat continuity, an adequate prey base, and

freedom from persecution (Mech 1995).

Human activity, large-scale industrial development, and human population

expansion have led to the extirpation of grey wolves (Canis lupus) throughout most of the

United States (Mech 1995, Clark et al. 1996). Through direct persecution, North

American wolf ranges have contracted to encompass about half of their historic range

(Mech and Boitaini 2003). However, current factors threatening the long-term survival

of extant wolf populations and other large carnivore populations are human disturbance

and habitat exploitation and alteration (Hummel and Pettigrew 1991, Paquet and

Hackman 1995, Clark et al. 1996).

A threshold or critical scale likely exists at which landscapes become fragmented

enough to restrict an individual’s movements or become unsuitable to occupy. When

habitat patches become, in effect, islands, such as many North American national parks,

restriction of movement of large carnivores such as wolves is of special concern

(Newmark 1995, Woodruffe and Ginsberg 1998). Human pressures such as roads,

hunting, and trapping that surround many protected areas likely influence the dispersal

ability of wolves.

1

1.2 Dispersal and inter-population movement

The ability of animals to move across the landscape often influences gene flow in

populations. At the population level, dispersal may be regarded as inter-population gene

flow or gene migration (Slatkin 1987). Population structure (Chessier et al. 1993),

genetic diversity (Hedrick 1995, Paetkau et al. 1998), and inter population source-sink

dynamics (Dias 1996) are all dispersal-mediated. Community structure and function

across a landscape are also products of dispersal at the ecosystem level (Mouquet et al.

2001).

Ultimate benefits for dispersal are inbreeding avoidance and lower competition

for mates and resources. Wolves live in socially structured family groups generally

consisting of a mated pair, their offspring, and older adult offspring helpers from

previous generations (Mech and Boitani 2003). Unrelated individuals from neighboring

packs also make up a portion of a given pack (Lehman et al.1992). Therefore, most of

the individuals in the group are related to some degree.

Average territory sizes for wolf packs occupying forested habitats range from 185

km2 where prey densities are high, and up to 568 km2 where prey densities are low

(Paquet and Carbyn 2003). Therefore, wolves require vast spaces for foraging (Paquet

and Carbyn 2003). Also, wolves are highly vagile animals capable of dispersing ~ 1000

km (Gese and Mech 1991, Wydeven et al. 1995, Wabakken et al. 2001). During long-

range dispersal movements and regular home-range use, wolves in many regions of North

America encounter human induced landscape changes such as agriculture, clear cuts, and

roads, particularly when venturing outside of protected areas (Mladendoff et al. 1995,

Woodruffe et al. 1998).

Roads and cleared landscapes allow increased access to remote areas by humans,

thereby increasing hunting, trapping, and direct vehicle mortality of wolves. In southeast

Alaska, wolf harvest was significantly and positively correlated with the linear km and

density of roads (Person et al. 1996), where 44% of wolves were killed directly from the

road system. Several other studies have shown a strong relationship between road

density and wolf survival (Thiel 1985, Jensen et al. 1986, Mech et al. 1988, Fuller 1989).

Wolves recolonizing Wisconsin selected areas with low road density (<0.45 km/km2)

(Mladendoff et al. 1995). Wolves generally do not persist in areas with average road

2

densities greater that 0.6 km/km2 (Thiel 1985, Jensen et al. 1986, Mech et al. 1988, Fuller

1989).

Dispersal constraints, such as habitat fragmentation and its associated effects, can

affect the pattern of relatedness among wolves within an area. In Minnesota, wolf packs

are restricted to areas of protected woodland and dispersal is inhibited by habitat

discontinuities caused by agriculture and development (Lehman et al. 1992). Juvenile

wolves may be unable to disperse as easily across fragmented habitats and remain within

their natal pack system rather than joining an established neighbouring pack (Wayne et

al. 1995). Also, wolf dispersal occurs predominantly during winter months (Fuller 1989,

Gese and Mech 1991), coinciding with periods when they are vulnerable to trapping

pressure. Gese and Mech (1991) documented wolves leaving their natal territories as

pups or yearlings, suggesting they may be naïve to the dangers of roads or anthropogenic

pressures such as hunting and trapping. Furthermore, dispersal often follows pre-

dispersal forays (Messier 1985, Gese and Mech 1991) where wolves may experience the

negative effects of roads and hunting pressure, thereby discouraging dispersal events and

limiting gene flow. Accordingly, wolves that do disperse are likely at an increased risk

of mortality (Mech 1995). Differences among areas in the number of close relatives

shared between packs reflected habitat constraints on dispersal (Lehman et al. 1992).

Therefore, human influences affect the genetic variability of partially isolated populations

in disturbed areas (Wayne et al. 1995).

1.3 Influence of foraging ecology on wolf population structure

Carnivore densities and distribution often reflect resource abundance, which

generally relates to prey resources (Fuller and Sievert 2001, Carbone and Gittleman

2002). Short- and long-term changes in prey abundance and availability can act as major

factors influencing population viability of large carnivores (Fuller and Sievert 2001).

Natal-habitat dispersal or habitat imprinting, which may include the influence of prey,

has been demonstrated in coyotes (Canis latrans; Sacks et al. 2004), cuckoos (Cuculis sp;

Vogl et al. 2002), butterflies (Parnassius smintheus) (Keghobadi et al. 1999) and recently

in wolves (Carmichael et al. 2001, Geffen et al. 2004). When prey availability changes

within a habitat, indirect changes in behaviour also occur that contribute to observed

3

changes in carnivore densities (Fuller and Sievert 2001). For example, wolf pack

territory size was negatively correlated with white-tailed deer (Oidelcoileus virginianus)

density in Wisconsin (Wydeven et al. 1995). As wolf movements change with dynamic

prey populations, the genetic structure of wolf population may be influenced as result of

prey specialization (Carmichael et al. 2001).

1.4 Study objectives

The paucity of information about population genetics and foraging ecology of

wolves in central Saskatchewan prompted the need to collect baseline information on

genetic variation and population structure in this region, as well as diet composition. My

objectives were to evaluate the dietary and genetic patterns observed in protected versus

unprotected wolf populations in Saskatchewan. To accomplish this, I employed non-

invasive research techniques such as scat and hair collection for genetic and stable

isotopic analyses.

In chapter 2, I examined genetic variation and population structure of wolves in

and around PANP. I used 11 microsatellite loci to identify population structure and

diversity and make inferences about wolf dispersal capabilities at a broad scale. A highly

structured population might indicate limited movement among putative subpopulations.

I further investigate foraging ecology and prey selection by wolves in PANP

using scat analysis in Chapter 3. I evaluated the diet composition of wolves, assessed

patterns in prey selection, and discussed the results in the context of foraging theory and

risk-avoidance behaviour.

Lastly, In chapter 4, I examined wolf diets using stable isotope analysis to observe

individual and population variation in diet on a larger temporal scale and explore wolf

trophic relationships in the boreal forest.

1.5 Conservation need

Estimates of genetic population diversity and structure are crucial in conservation

biology where it is often necessary to understand whether populations are genetically

isolated from each other, and if so, to what extent (Johnson et al. 2001). Preserving

genetic diversity is important because of the long-term evolutionary potential it provides

4

populations and species. Rare alleles, or combinations of alleles, may confer no

immediate advantage but could be well suited to future environmental conditions

(Madsen et al. 1996, Saccheri et al. 1998, Higgins and Lynch 2001, Balloux and Lugon-

Moulin 2002). Furthermore, knowledge of population genetic structure provides valuable

guidelines for conservation strategies and management (Eizerik et al. 2000, Johnson et al.

2001). Links between habitat characteristics, including prey availability and use, provide

important baseline information giving context for any apparent structuring of

populations. Diet composition and trophic relationships provide insight into community

interactions and contribute to the knowledge from which conservation and management

decisions are drawn (Clark et al. 2001).

5

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Carbone, C. and J. L. Gittleman. 2002. A common rule for the scaling of carnivore density. Science 295: 2273-2276. Carmichael, L. E., J. A. Nagy, N. C. Larter and C. Strobeck. 2001. Prey specialization

may influence patterns of gene flow in wolves of the Canadian Northwest. Molecular Ecology 10: 2787-2798.

Chessier, R. K., O. E. J. Rhodes, D. W. Sugg, and A. Schnabel. 1993. Effective sizes

for subdivided populations. Genetics 135: 1221-1232. Clark, T. W., D. J. Mattson, R. P. Reading, and B. J. Miller. 2001. Interdisciplinary

problem solving in carnivore conservation: and introduction. Pages 223-240 in Gittleman, J. L., Funk, S. M., MacDonald, D. W., and Wayne, R. K., editors. Carnivore Conservation. Cambridge University Press, Cambridge.

Clark, T. W., P. C. Paquet, and A .P. Curlee. 1996. Large carnivore conservation in the

Rocky Mountains of the United States and Canada. Conservation Biology 10: 936-939.

Dias, P. C. 1996. Sources and sinks in population biology. Trends in Ecology and

Evolution 11: 326-330. Eizerik, E., C. B. Indrusiak, W. E. Johnson. 2000. Jaguar population viability analyses: evaluation of parameters and a case study of a remnant population in a South American subtropical rain forest. In T. A. Medellin, C. Chetkiewicz, A.

Rabinowitz, K. H. Redford, J. G. Robinson, E. Sanderson, and A. Taber, editors. Jaguars in the New Millennium: A Status Assessment, Priority Detection and Recommendation for the Conservation of Jaguars in the Americas. Universidad Nacional Autonoma de Mexico/Wildlife Conservation Society.

Forbes, S. H. and D. K. Boyd. 1997. Genetic structure and migration in native and

reintroduced Rocky mountain wolf populations. Conservation Biology 11: 1226 1234. Fritts, S. H. and L. N. Carbyn. 1995. Population viability, nature reserves, and the

outlook for gray wolf conservation in North America. Restoration Ecology 3: 26-38.

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Fuller, T. K. 1989. Population dynamics of wolves in north-central Minnesota. Wildlife Monogographs 105. 41pp. Fuller, T. K. and P. R. Sievert. 2001. Carnivore demography and the consequences

of changes in prey availability. Pages 163-178 in J. L Gittleman, S. M. Funk, D. Macdonald and R. K. Wayne, editors. Carnivore Conservation. Cambridge University Press, Cambridge, UK.

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dispersal in the highly mobile gray wolf. Molecular Ecology in press. Gese, E. M. and L. D. Mech. 1991. Dispersal of wolves (Canis lupus) in northeastern Minnesota, 1969-1989. Canadian Journal of Zoology 69: 2946-2955. Gotelli, D., C. Sillero-Zubiri, G. D. Applebaum, M. S. Roy, D. J. Girman, J. Garcia

Moreno, E. A. Ostrander, and R. K. Wayne. 1994. Molecular genetics of the most endangered canid: the Ethiopian wolf Canis simensis. Molecular Ecology 3: 301-312.

Hedrick, P. W. 1995. Gene flow and restoration: the Florida panther as a case study.

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accumulation. Proceedings of the National Academy of Sciences USA 98: 2928-2933.

Hummel, M. and S. Pettigrew. 1991. Wild hunters: predators in peril. Key Porter Books Ltd., Toronto. Jensen, W. F., T. K. Fuller, and W. L. Robinson. 1986. Wolf (Canis lupus) distribution

on the Ontario to Michigan border near Sault Ste. Marie. Canadian Field. Naturalist 100: 363-366.

Johnson, W. E., E. Eizirik, M. Roelke-Parker, and S. J. O’Brien. 2001. Applications of

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Keyghobadi, N., J. Roland, C. Strobeck. 1999. Influence of landscape on the

population genetic structure of the alpine butterfly Parnassius smintheus (Papilionidae). Molecular Ecology 8: 1481-1495.

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Evolution 12: 223-227. Laikre, L. and N. Ryman. 1991. Inbreeding depression in a captive wolf (Canis lupus) population. Conservation Biology 5: 33-40. Lehman, N., P. Clarkson, L. D. Mech, T. J. Meier, and R. K. Wayne. 1992. A study of

the genetic relationships within and among wolf packs using DNA fingerprinting and mitochondrial DNA. Behavioral Ecology and Sociobiology 30: 83-94.

Mace, G. M., T. B. Smith, M. W. Bruford, and R. K. Wayne. 1996. Molecular genetic approaches in conservation-an overview of the issues. Pages 1-12 in T. B. Smith and R. K. Wayne, editors. Molecular Genetic Approaches in Conservation.

Oxford University Press, Oxford. Madsen, T., B. Stille, and R. Shine. 1996. Inbreeding depression in an isolated

population of adders, Vipera berus. Biological Conservation 75: 113-118. Mech, L. D. 1995. The challenge and opportunity of recovering wolf populations.

Conservation Biology 9: 270-278. Mech, L. D., S. H. Fritts, G. L. Radde, and W. J. Paul. 1988. Wolf distribution and road density in Minnesota. Wildlife Society Bulletin 16: 85-87. Meier, T. J., J. W. Burch, L. D. Mech and L. G. Adams. 1995. Pack structure and

genetic relatedness among wolf populations in a naturally-regulated population. Pages 293-302 in L. N. Carbyn, S. H. Fritts, and D. R. Seip, editors. Ecology and Conservation of Wolves in a Changing World. Canadian Circumpolar Institute, Edmonton, AB, Canada.

Messier, F. 1985. Solitary living and extraterritorial movements of wolves in relation to social status and prey abundance. Canadian Journal of Zoology 63: 239-245. Mladendoff, D. J., T. A. Sickley, R. G. Haight, and A. P. Wydeven. 1995. A regional landscape analysis and prediction of favorable gray wolf habitat in the northern Great Lakes region. Conservation Biology 9: 279-294. Mouquet, G. S., E. E. Mulder, V. A. A. Jansen, and M. Loreau. 2001. The properties

of competitive communities with coupled local and regional dynamics. Pages 311-328 in J. Clobert, E. Danchin, A. A. Dhondt and J. D. Nichols, editors. Dispersal. Oxford University Press, Oxford, UK.

Newmark, W. D. 1995. A land-bridge perspective on mammalian extinctions in western North American national parks. Conservation Biology 9: 512-526. Paetkau, D. H., G. F. Shields and C. Strobeck. 1998. Gene flow between insular, coastal

and interior populations of brown bears in Alaska. Molecular Ecology 7: 1283-

8

1292. Paquet, P. C. and A. Hackman. 1995. Large carnivore conservation in the Rocky Mountains: a long-term strategy for maintaining free-ranging and self- sustaining populations of carnivores. World Wildlife Fund (Canada), Toronto, ON. Paquet, P. C. and L. N. Carbyn. 2003. Wolf, Canis lupus. Wild mammals of North

America: Biology, Management, and Conservation. In G. A. Feldhammer, B. C. Thompson and J. A. Chapman. Johns Hopkins University Press. Baltimore, MD, USA.

Person, D. K., M. Kirchoff, V. Van Ballenberghe, G. V. Iverson, and E. Grossman.

1996. The Alexander Archipelago wolf: a conservation assessment. United States Department of Agriculture-Forest Service. General Technical Report. PNWGTR-384. Portland, Oregon, USA.

Randi, E., V. Lucchini, and F. Francisci. 1993. Allozyme variability in the Italian wolf (Canis lupus) population. Heredity 71: 516-522. Reed, J. Z., D. J. Tollit, P. M. Thompson, and W. Amos. 1997. Molecular scatology: the use of molecular genetic analysis to assign species, sex and individual identity to seal faeces. Molecular Ecology 6: 225-234. Saccheri, I., M. Kuussaari, M. Kankare, P. Vikman, W. Fortelius, and I. Hanski. 1998. Inbreeding and extinction in a butterfly metapopulation. Nature 392: 491-494. Sacks, B. N., S. K. Brown, and H. B. Ernest. 2004. Population structure of California

coyotes corresponds to habitat specific breaks and illuminates species history. Molecular Ecology 13: 1265-1275.

Saunders, D. A., R. J. Hobbs, C. R. Margules. 1991. Biological consequences of

ecosystem fragmentation: a review. Conservation Biology 5: 18-32. Slatkin, M. 1987. Gene flow and the geographic structure of natural populations.

Science 236: 787-792. Theil, R. P. 1985. The relationship between road densities and wolf habitat suitability in Wisconsin. American Midland Naturalist 113: 404-407. Vogl, W., M. Taborsky, B. Taborsky, Y. Teuschl, and M. Honza. 2002. Cuckoo

females preferentially use specific habitats when searching for host nests. Animal Behavior 64: 843-850.

Wabakken, P., H. Sand, O. Liberg, A. Bjarvall. 2001. The recovery, distribution, and

population dynamics of wolves on the Scandinavian peninsula, 1978-1998.

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Canadian Journal of Zoology 79: 710-725. Wayne, R. K. 1992. Mitochondrial DNA variability of the gray wolf: genetic consequences of population decline and habitat fragmentation. Conservation

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L. D. Mech and L. Boitani, editors. Wolves: behavior, ecology, and conservation. University of Chicago Press, Chicago, USA.

Wayne, R. K., N. Lehman, and T. K. Fuller. 1995. Conservation genetics of the Gray

wolf. Pages 399-408 in L. N. Carbyn, S. H. Fritts, and D R. Seip, editors. Ecology and Conservation of Wolves in a Changing World. Canadian Circumpolar Institute, Edmonton, AB, Canada.

Wayne, R. K., N. Lehman, D. Girman, P. J. P. Gogan, D. A. Gilbert, K. Hansen, R. O. Peterson, U. S. Seal, A. Eisenhawer, L. D. Mech and R. J. Krumenaker. 1991. Conservation genetics of the endangered Isle Royale Gray wolf. Conservation

Biology 5: 41-51. Wiens, J. A. 2001. The landscape context of dispersal. Pages 96-109 in J. Clobert, E.

Danchin, A. A. Dhondt, and J. D. Nichols, editors. Dispersal. Oxford University Press, Oxford, UK.

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fragmentation on extinction. American Naturalist 125: 879-887. With, K. A. 2004. Metapopulation dynamics: perspectives from landscape ecology.

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Woodroffe, R. and J. R. Ginsberg. 1998. Edge effects and the extinction of populations inside protected areas. Science 280: 2126-2128. Wydeven, A. P., R. N. Schultz, and R. P. Thiel. 1995. Monitoring of a gray wolf

population in Wisconsin. 1979-1991. Pages 147-156 in L. N. Carbyn, S. H. Fritts, and D. R. Seip, editors. Ecology and conservation of wolves in a changing world. Canadian Circumpolar Institute, Edmonton, AB, Canada.

10

2.0 GENETIC DIVERSITY AND DIFFERENTIATION OF GREY

WOLF POPULATIONS IN CENTRAL SASKATCHEWAN

2.1 Introduction

Central to evolutionary ecology and conservation biology is the investigation of

animal movement and dispersal in rapidly changing landscapes (Young and Clarke

2000). Habitat fragmentation, anthropogenic development, and prey distribution

influence animal movements and genetic structure of animal populations, including the

propensity for inbreeding (Wayne et al. 1992, Young and Clarke 2000). Of special

concern are wide-ranging, long-lived carnivores that require vast spaces of intact habitat

(Gittleman et al. 2001).

Ranging throughout most of the Northern Hemisphere, grey wolves are

considered one of the most vagile predators (Mech 1970, Fritts 1983, Merrill and Mech

2000, Paquet and Carbyn 2003). Wolves occur in diverse ecosystems from desert to

dense forest and arctic islands (Mech 1970, Nowak 1999). They disperse over great

distances and topographic barriers to find mates and territories (Mech and Boitani 2003).

However, at some geographic scale, distance prevents the exchange of individuals

between populations and is conceptually an important impediment to dispersal. Patterns

of isolation with distance are not expected for highly mobile and territorial canids like

grey wolves (Chepko-Sade1987). Previous studies support this expectation by

demonstrating a lack of correlation between genetic differentiation and distance in grey

wolves (Wayne et al. 1992, Roy et al. 1994, Vila et al. 1999). However, Forbes and

Boyd (1997) elucidated such a correlation at a finer geographic scale. In addition to

isolation by distance, population structuring can also result over climatic clines (Geffen et

al. 2004) and in areas where the prey base dramatically changes and wolves specialize on

those prey (Carmichael et al 2001, Musiani 2003). Wolf populations can also be adapted

to local conditions and show specializations concerning den-site use, foraging habitats,

and physical environment (Carmichael et al. 2001, Musiani 2003, Geffen et al. 2004).

11

North American wolf populations are generally considered genetically diverse

(Roy et al 1994). However, recent genetic work has eluded that historical (pre-

extermination) wolves show more than twice the diversity of their modern conspecifics

(Leonard et al. 2004). A two-thirds loss of unique genetic haplotypes resulted due to the

large scale extermination of wolves in the coterminous United States (Leonard et al.

2004). Further, loss of genetic variation in small and isolated wolf populations has been

shown and is of special concern (Wayne et al 1992, Randi et al. 1993, Gotelli et al. 1994,

Fritts and Carbyn 1995, Wayne and Vila 2003).

Gene flow and population structure are largely determined by natal dispersal

(Taylor and Taylor 1977, Slatkin 1987). Short-distance (1-100 km) dispersal is common

in wolf populations (Gese & Mech 1991, Messier 1985a) and there have been

documentations of wolves dispersing upwards of ~ 1000 km (Van Camp & Gluckie 1979,

Fritts 1983, Boyd et al 1995, Wabakken 2002). Long distance dispersal events may be

more common than we think due to the low probability of detecting dispersers.

Originally distributed province-wide, current wolf range in Saskatchewan,

Canada, scarcely extends past the southern limit of the diminishing parkland and boreal

ecosystems. As forests rapidly recede (Hobson et al. 2002, Fitzsimmons 2002), habitat

for wolves continues to diminish, increasing the risk of wolf-livestock conflict, hunting

access, and road mortality. Rates of large-scale forest clearing for agricultural purposes

and logging are high in the fringe of the boreal forest (Hobson et al 2002, Fitzsimmons

2002). For many species of large carnivore, even within protected areas like parks,

human conflicts are the most important cause of mortality (Newmark 1995, Woodruffe &

Ginsberg 1998, Callaghan 2002). When wolves attempt to move beyond protected area

boundaries, such as would be the case during extraterritorial forays that precede dispersal

and dispersal itself (Messier 1985), risk of mortality is presumably high (Mech 1995).

Prince Albert National Park (PANP), the only protected wolf habitat in Saskatchewan, is

at increasing risk of isolation due to the above factors.

My objectives were to: 1) evaluate levels of genetic variation found in wolf

populations inhabiting central Saskatchewan, and 2) elucidate the genetic population

structure of wolves across a potentially fragmented landscape. I predicted high levels of

natural genetic variation, relative to extant North American wolf populations, within

12

these populations because the relatively contiguous nature of the landscape would allow

wolves to mix freely. I also predicted that wolves in central Saskatchewan would retain

low levels of genetic subdivision among populations given their mobility, home range

size, and dispersal capability.

2.2 Methods

2.2.1 Study area

My study area was in central Saskatchewan (Figure 2.1). PANP, the core study

area, harbours a wolf population (~2 wolves/100 km2 - Paquet & Carbyn 2003) dependent

on a multi-prey system. Predominantly boreal forest, PANP is 3,875 km2 and the largest

protected wilderness in Saskatchewan. Cultivated land, grazing areas, forest harvesting,

road construction, and fur-trapping lines are common on adjacent provincial lands. For a

detailed description of the study area see Chapter 3.

2.2.2 Non-invasive research

Approaches to population genetics have recently expanded to include non-

invasive research techniques (Palsbøll 1999, Kohn and Wayne 1999, Luccini et al. 2002).

The ability to isolate and analyse DNA from opportunistically collected hair and faeces

has allowed scientists to produce genetic information as detailed as identities for

individuals in a population (Kohn et al. 1999, Ernest et al. 2002, Luccini 2002),

facilitating investigation into population genetic structure, genetic history, and population

size estimation (Kohn et al. 1999, Wayne and Vila 2003).

The use of microsatellite markers has advanced genetic investigation of animal

populations (Balloux and Lugon-Moulin 2002). Microsatellites are single-locus genetic

markers consisting of simple sequence motifs (Kohn & Wayne 1997). They are neutral,

co-dominantly inherited, highly polymorphic, and scattered throughout the genome of all

living organisms. Using polymerase chain reaction (PCR), these markers can be

13

Figure 2.1. Study area in the boreal forest of central Saskatchewan, Canada.

14

amplified from small and degraded sources of DNA and are ideal for studying genetic

variation, population structure, and gene flow in many free ranging animal populations

(Kohn & Wayne 1997, Kohn et al 1999). DNA microsatellites cloned from the domestic

dog (Canis familiaris) genome offer the ability to obtain new insights into grey wolf

population genetics (Ostrander et al.1993, Roy et al. 1994, Forbes and Boyd 1996).

Using these techniques, capture and handling of animals is eliminated, making it effective

for investigating endangered and difficult to capture species.

2.2.3 Genetic samples

I sampled wolf faeces (scats) throughout PANP and surrounding areas in 2003.

Wolf scats were collected wherever encountered on trails, lakeshores, other linear

features, and by back-tracking wolves. GPS location (UTM) and date were recorded and

samples were stored frozen in plastic bags. Scats were differentiated from those of

sympatric coyotes based on associated signs and tracks, scat size (>30 mm in diameter

for wolves), and appearance. I collected only intact and relatively fresh scats in summer.

I also collected tissues from wolf mortalities that occurred within and outside PANP.

One hundred and 50 scats and 16 hide samples were available for analyses. Putative

populations were defined by sampling location (Figure 2.2). I schematically divided

PANP into 4 quadrants; I assigned samples collected in the northeast quadrant to the NE

group, the northwest samples to the NW group, the southeast samples to the SE group,

the southwest samples to the SW group, and the La Ronge samples to the LR group.

2.2.4 DNA extraction

I extracted and isolated DNA from scats using a Qiagen® DNA mini stool kit,

and from tissue samples using a Qiagen® DNeasy Tissue kit following manufacturer

protocols. I quantified DNA in sample using fluorometry. Test results of poorly

amplifying scat DNA indicated a lack of wolf specific target DNA present. I used

negative controls throughout.

15

Figure 2.2. Wolf scat sample locations in central Saskatchewan (not all samples are

shown).

16

2.2.5 Identification of microsatellite alleles

DNA markers used in the screen are shown in Table 2.1. A positive control plus

2 samples representing sample type (tissue and scat) were tested against 13 fluorescently

labeled microsatellite markers using PCR. Original PCR conditions optimized for all 13

markers were Mg++ concentration, annealing temperature, and primer concentration.

Additional conditions optimized to attain scat DNA amplification were DNA

concentration, Taq concentration, and the number of PCR program cycles and length of

extension step. Stringent conditions used to genotype “good” samples were: 1.5 to 2.0

mM MgCl2, 55° annealing temperature, 35 cycles, 1 U Taq per rxn; Liberal conditions

used to genotype “poor” samples were: 1.5 to 2.0 mM MgCl2, 55° annealing temperature,

45 cycles, 2 U Taq per rxn. DNA fragments were resolved using the LICOR™ 4200

DNA Analyzer system, and scored using GeneImagIR (LICOR™). PCR artefacts such as

allelic dropout and generation of false alleles may obscure true patterns of diversity due

to dilution and/or degradation of faecal DNA. PCR on duplicate sample of faecal

material clarified such inconsistencies in genotyping. Low volumes of some samples

prevented complete testing with all markers or supplemental extractions of DNA.

2.2.6 Microsatellite diversity, population relationships and gene flow Genetic polymorphism of each population was measured as the mean number of

alleles per locus (A), the observed heterozygosity (HO), and the expected heterozygosity

from Hardy-Weinburg (H-W) assumptions (HE; Nei 1978, 1987). I used HE because it is

a less biased index of genetic variability and is highly correlated with HO (Nei and

Roychoudhury 1974). Heterozygosity statistics were calculated by GENETIX (Belkhir et

al. 2000) with a test based on Black and Krafsur (1985).

Spatial structure in microsatellites was examined by conventional F- statistics

using the estimator theta (θ) (Weir and Cockerham 1984). Theta measures the proportion

of total variation due to differences between subpopulations. I used GENETIX (Belkhir et

al. 2000) to perform these calculations. Significances of θ were calculated after 1000

permutations. Matrices of pairwise θ-values between groups were calculated for the data

and the level of significance for the values was estimated through a randomization test (as

implemented by the program GENETIX). A matrix of pair-wise Euclidean distances

17

Table 2.1. Sequences of the 13 microsatellite DNA markers used in genotyping wolf

samples.

Name Forward Primer Reverse Primer

PEZ01 ggctgtcacttttccctttc caccacaatctctctcataaatac

c09.250 ttagttaacccagctcccca tcaccctgttagctgctcaa

FH2054 gccttattcattgcagttaggg atgctgagttttgaactttccc

VWF.x ctccccttctctacctccacctctaa cagaggtcagcaagggtactattgtg

c20.253 aatggcaggattttcttttgc atctttggacgaatggataagg

FH2001 tcctcctcttctttccattgg tgaacagagttaaggatagacacg

FH2096 ccgtctaagagcctcccag gacaaggtttcctggtccca

FH2010 aaatggaacagttgagcatgc ccccttacagcttcattttcc

FH2017 agcctctataatcacgtgagcc cccagtaccaccttcaggaa

*FH2006 tgggggcgttaagagtaatg ctaggcctaaacccctgagc

PEZ08 tatcgactttatcactgtgg atggagcctcatgtctcatc

*MS34b agccattcctggccgagtcc ggtccccttttgccatagtgt

MS41b** tcctctaattttcccctctc ctgctcgaccctcttctctg

* these markers were dropped from the analysis

** y-chromosome marker (sex-linked)

18

between the central locations (UTM) for each wolf group was also calculated. All

matrices were tested for correlation using Mantel’s test (Liedloff 1999). Genetic

relationships among groups were calculated for microsatellite DNA using Nei’s genetic

distance measure, Ds (Nei 1978). The number of migrants per generation, Nm, was

estimated from θ-values by the expression θ = 1/ (1 + 4 Nm). A neighbor-joining tree

was inferred from the Ds values using PHYLIP 3.57 (Felsenstein 1993). The pattern of 2-

dimensional spatial structuring of groups was represented through Multidimensional

Scaling (MDS) performed on the matrix of pairwise θ-values obtained from

microsatellite data (SPSS, version 12.5).

2.3 Results

2.3.1 DNA extraction

The results showed amplification with only the controls and tissue sample for 11

of the markers. Marker MS34b did not amplify and was dropped from the analysis.

Marker FH2006 was monomorphic and also dropped. Quantification of representative

samples showed low DNA concentrations in samples prepared from scats. I eliminated

poor quality samples and continued with relatively good samples. Thus, 75 out of 150

scat samples and 16 tissue samples were successfully genotyped using 11 microsatellite

markers. Seventy-one individual wolves that I genotyped were sampled from within

PANP, and 16 individuals used other areas next to the park (87 individuals total). I found

4 matching genotypes among these indicating that some individuals were sampled more

than once. The grand total of individual wolves was 83; 62% were males and 38% were

females. Subsequent analyses for genetic diversity and population structure were

performed using 10 loci; sex-linked marker MS41B was removed to prevent a bias in

heterozygosity measures.

2.3.2 Microsatellite diversity

I found consistent deviations from H-W equilibrium at locus PEZ01 and locus

MS41b with deficiency in observed heterozygosity (p < 0.001). In addition, locus

c09.250 deviates from H-W equilibrium in 4 out of 5 putative groups (p < 0.001). When

19

putative populations are grouped together, all loci do not fulfill H-W equilibrium, likely

due to heterozygosity deficiency, indicating that this is not a single panmictic population.

The observed levels of genetic variation are presented in Table 2.2. The mean

number of alleles per locus ranged from 4.0 for the NW group to 6.0 for the SE group.

Heterozygosity values (HE) were high relative to other North American wolf populations

(Forbes and Boyd 1997) and similar among groups, ranging from 0.5484 for the NW

group to 0.6612 for the LR group.

2.3.3 Population structure and gene flow

Microsatellite matrices of θ-distances and Ds (Table 2.3) were not correlated with

a matrix of geographic distances (km) between subgroups as indicated by Mantel tests (r

= 0.30, p = 0.32; r = 0.58, p = 0.10, respectively). I used pairwise Nm-values to estimate

the number of individuals that migrate between each pair of sampling localities (Table

2.4). The SE and SW groups had the highest values of Nm, whereas the NW had the

lowest. I found no correlation between Nm values and geographical distance between

localities (Mantel’s test; r = -0.32, p = 0.73). Genetic structuring of wolf groups was also

captured by an MDS plot (Figure 2.3); the NW wolf group was segregated from all other

groups along dimension 1. The topology of populations illustrated by a neighbor-joining

tree (Figure 2.4) was consistent with genetic structuring seen in the MDS plot.

2.4 Discussion

Wolves in central Saskatchewan exhibit relatively low levels of genetic structure,

with the exception of the NW group, which is separate from all other groups. Moreover,

the observed structure is not a result of isolation by distance. I expected this because

wolves are highly vagile animals. All other groups were genetically similar suggesting

that individuals mix freely. Genetic variation was high in these groups as was the

number of migrants when compared with the NW group and other wolf populations in

North America (Forbes and Boyd 1996, Lehman et al. 1992). High Nm, low Fst, high

allelic diversity, and high levels of heterozygosity (HE) suggest high genetic turnover in

the SE and SW groups.

20

Table 2.2. Genetic variation at 10 microsatellite loci in central Saskatchewan wolves.

Population n Mean no. of alleles Ho He

NE 16 4.9 0.428 0.597

NW 20 4.0 0.476 0.548

SE 27 6.0 0.509 0.656

SW 11 5.0 0.544 0.591

LR 13 5.0 0.600 0.661

Table 2.3. Pairwise genetic distances (lower; Nei 1978) and Fst (θ) (upper) among central

Saskatchewan wolves.

NE NW SE SW LR

NE 0 0.0735 0.0119 0.0237 0.0560

NW 0.130 0 0.0515 0.0744 0.0887

SE 0.034 0.093 0 0.0221 0.0275

SW 0.057 0.128 0.056 0 0.0695

LR 0.130 0.168 0.074 0.162 0

Table 2.4. Pairwise number of migrants (Nm) among 5 grey wolf populations in central

Saskatchewan.

NE NW SE SW LR

NE 0 3.2 20.8 10.3 4

NW 0 0 4.6 3.1 2.7

SE 0 0 0 11 10

SW 0 0 0 0 3.7

LR 0 0 0 0 0

21

MDS - Theta values

Dimension 1

1.51.0.50.0-.5-1.0-1.5-2.0-2.5

Dim

ension

2

1.5

1.0

.5

0.0

-.5

-1.0

-1.5

-2.0

SW

NE SE

MDS – Theta (θ) values

NW

LR

Figure 2.3. Pattern of 2-dimensional spatial structuring of wolf groups represented

through Multidimensional Scaling (MDS) performed on the matrix of pairwise θ values

obtained from microsatellite data.

22

Figure 2.4. Neighbor-joining tree inferred from Nei’s genetic distance measures

calculated for microsatellite data.

100

lr

se

sw

ne

376

nw 2NW

396

1

NE

5 LR

4 SW

3SE

23

Genetic structure of wolves from PANP and surrounding area suggest that human

pressures like hunting and trapping may influence dispersal. Although there is a change

in habitat type, and potentially prey distribution and abundance between PANP and Lac

La Ronge, I found no significant genetic structure among these wolf groups. The partial

isolation of the NW group may be explained by several ecological factors. Most wolf

packs consist of a mated pair, their offspring, and adult helper offspring from previous

years. Therefore, most members of a pack are closely related (Mech and Boitani 2003).

These familial relationships have been confirmed using microsatellite analysis of social

groups from Minnesota and Denali National Park, Alaska (Lehman et al. 1992, Meier et

al. 1995). Some offspring, however, dispersed into neighboring packs or formed new

packs nearby. No such inter-pack similarity was found in the Inuvik region of Canada’s

arctic, where wolves are heavily hunted, suggesting that higher genetic turnover may

account for the absence of close family relationships (Lehman et al. 1992). In that study,

one heavily exploited population had fewer kinship ties and more genetic turnover than

two protected ones (Lehman et al. 1992).

Further, studies on temporal genetic variation in pack-forming coyotes during

periods of locally aggressive removal indicated that exploitation perpetuated genetic

homogeneity over relatively small geographic scales (Williams et al 2003). However,

effects on social structure were unknown.

Wolves in the NW of PANP are less affected by hunting and trapping than other

wolves in my study region because of limited access to the area by roads and trails. In

addition, human activity and structures are uncommon or non-existent in this area. In

comparison, all other populations are near primary (highway) and secondary roads

supporting much higher levels of human activity. Accordingly, combined human

pressures may be contributing to the genetic homogenization of wolf packs as follows.

The pack(s) in the NW group may remain “quiet” with limited dispersal and high

stability, whereas turnover in other groups may invite more dispersers causing pack

instability and influencing the breeding pairs in those packs. If inter-pack kinship affects

social stability and pack persistence (Wayne 1996), reducing the effects of wolf

exploitation and road mortality on genetic population structure may be a conservation

priority (Leader-Williams et al. 2001).

24

The observed structure could also be a function of sampling bias. The heavily

sampled SE region of the park might be high quality habitat where a number of different

packs overlap spatially but are segregated temporally. I defined groups as 1 or 2 packs

using each region (quadrant). However, 3, 4, or 5 packs could be using an area at

different times. Moreover, the size and shapes of territories may be structured so that all

of them overlap already.

In comparison with other North American wolf populations, the observed

structure in my study area was surprising. Genetic differentiation among North

American wolf populations was low to moderate suggesting that exchange of a few

individuals per generation is enough to prevent appreciable genetic structure by random

drift (Roy et al. 1994a, 1994b, Forbes and Boyd 1996). Carmichael et al. (2001) and

Musiani (2003) suggest that population differentiation occurred in Canada’s Northwest

because of ecological specialization of wolves preying on different caribou herds.

Similarly, the observed population subdivision of wolves in the NW region of PANP

might reflect specialized use of some undocumented resource.

Genetic diversity of wolves in central Saskatchewan is high, in keeping with

other North American populations (Boyd et al. 1997, Lehman et al. 1992, Roy et al.

1994, Carmichael et al. 2001). However, given increasing habitat loss and fragmentation,

a future decline in genetic variation is likely (Ginsburg and Woodruffe 1997, Wayne and

Vila 2003), especially considering historical genetic diversity has decreased by 50% since

the historical mass persecution of wolves (Leonard et al. 2004). To prevent further

declines, population sizes should be kept as large as possible. Further, intact habitat

linkages should facilitate gene flow among populations. The loss of genetic variation in

isolated wolf populations is of great concern. Wolves on the 450 km2 Isle Royale,

isolated by over 10 km of water, have levels of average relatedness approaching those of

inbred captive populations and could suffer a decrease in fitness adversely affecting

population persistence (Mace et al. 1996, Hedrick and Kalinowski 2000). Inbreeding

depression in captive wolves has been documented (Laikre and Ryman 1991, Laikre et al.

1993, Fredrickson and Hedrick 2002) and implies dire consequences should this occur in

wild populations. Maintaining genetic variation, particularly components that influence

25

fitness, is vital to population persistence and the impending evolutionary response of

animals to changing environmental conditions (Crandall et al. 2000).

26

2.5 References

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Saskatchewan. Canadian Plains Research Centre, Saskatchewan Environment And Resource Management, Regina, Saskatchewan.

Balloux, F. and N. Lugon-Moulin. 2002. The estimation of population differentiation with microsatellite markers. Molecular Ecology 11: 155-165. Belkhir, K., P. Borsa, L. Chikhi, N. Raufaste, F. Bonhomme. 2000. GENETIX 4.0,

logiciel sous Windows TM pour la génétique des populations. Laboratoir Génome, Populations, Interactions, CNRS UPR 9060, Université de Montpellier II, Montpellier France.

Black, W. C. and Krafsur, E. S. 1985. A FORTRAN program for the calculation and analysis of 2-locus linkage disequilibrium coefficients. Theoretical and Applied Genetics 70: 491-496. Boyd, D. K., P. C. Paquet, S. Donelon, R. R. Ream, D. H. Pletscher, and C.C. White.

1995. Transboundary movements of a colonizing wolf population in the Rocky Mountains. Pages 135-140 in L. N. Carbyn, S. H. Fritts, and D. R. Seip, editors. Ecology and Conservation of Wolves in a Changing World. Canadian

Circumpolar Institute, Edmonton, Alberta. Callaghan, C. J. 2002. The ecology of gray wolf (Canis lupus) habitat use, survival, and persistence in the central Rocky Mountains, Canada. Ph. D. Thesis. University of Guelph, ON, Canada. Carmichael, L. E., J. A. Nagy, N. C. Larter and C. Strobeck. 2001. Prey specialization

may influence patterns of gene flow in wolves of the Canadian Northwest. Molecular Ecology 10: 2787-2798.

Chepko-Sade, B. D., W. M. Shields, J. Berger, Z. T. Halpin, W. T. Jones, L. L. Rogers, J. P. Rood and A. T. Smith. 1987. The effects of dispersal and social structure on effective population size. Pages 297-321 in B. D. Chepko-Sade and Z. T. Halpin, editors. Mammalian dispersal patterns. University of Chicago Press, Chicago, USA. Crandall, K. A., O. R. P. Bibida-Emonds, G. M. Mace, and R. K. Wayne. 2000.

Considering evolutionary processes in conservation biology. Trends in Ecology and Evolution 15: 290-295.

Ernest, H. B., E. S. Rubin, and W. M. Boyce. 2002. Fecal DNA analysis and risk assessment of mountain lion predation of bighorn sheep. Journal of Wildlife

Management 66: 75-85.

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Felenstein, J. 1993. PHYLIP, Phylogeny Inference Package version 3.5c. Distributed by the author. Department of Genetics, University of Washington, Seattle, USA. Fitzsimmons, M. 2002. Estimated rates of deforestation in two boreal landscapes in

central Saskatchewan, Canada. Canadian Journal of Forest Research 32: 843-951.

Forbes, S. H. and D. K. Boyd. 1996. Genetic variation of naturally colonizing wolves in the central Rocky mountains. Conservation Biology 10: 1082-1090. Forbes, S. H. and D. K. Boyd. 1997. Genetic structure and migration in native and

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pages.

32

3.0 FORAGING ECOLOGY AND PREY SELECTION BY GREY

WOLVES

3.1 Introduction

Foraging decisions comprise one of the major determinants of various life-history

strategies in predators, including spacing pattern, movement, habitat selection, social

structure, reproduction, and geographical distribution (Krebs 1978, Bekoff et al. 1984).

Predation profoundly influences ecosystem processes, influences natural selection, and

structures ecological communities (Kotler et al. 1994, Kie 1999, Terborgh et al. 2001).

The non-random nature of predation is fundamental in shaping prey community structure

and plays a key role in perpetuating plant and animal diversity (Sih et al.1993, Terborgh

et al. 1999, 2001). Predators often focus on infirm and naïve prey (FitzGibbon 1990) and

some prefer one or more prey species to all species available (Huggard 1993, Karanth

and Sunquist 1995). According to optimal foraging theory, predators should select

predominantly for prey that are most profitable, irrespective of their densities or densities

of other prey species (MacArthur and Pianka 1966, Chesson 1983, Krebs et al. 1983,

Begon et al. 1996).

Variation in predation patterns are influenced by habitat characteristics,

environmental conditions, prey assemblages, distribution, physical vulnerability, and

defensive behaviour of prey, and risk of injury or mortality to the predator (Begon et al.

1996). Prey selection is a balance among profitability of prey items and their availability

in the environment (Stephens and Krebs 1986, Forbes 1989). Forbes (1989) posited the

dangerous prey hypothesis where handling behaviour is a function of the risk of injury to

the predator. Hence, more dangerous prey should be handled more carefully (increasing

time and energy demands) or not at all. Predators should then take dangerous prey less

often because of longer handling times (Stephens and Krebs 1986: 128 - 150).

Grey wolves, as summit carnivores, feed on a wide variety of prey ranging in size

from 1 kg to 1000 kg (Mech and Boitani 2003). Typically, wolves prey upon ungulates

33

in all the ecosystems in which they occur (Poulle et al 1997, Jedrzejewski et al 2000,

Mech and Boitani 2003), however they do not necessarily select for the largest ungulate

available (Huggard 1993a). Wolves prey principally on moose (Alces alces) in Quebec,

Yukon, and Isle Royale National Park (Messier 1985a, 1985b, Messier and Crete 1985

Peterson and Page 1988); deer in Algonquin Provincial Park, Canada, and Minnesota

(Mech 1966, Fritts and Mech 1981, Fuller 1989), and muskox (Ovibos moshatus) and

caribou (Rangifer tarandus) on Arctic islands (Miller 1995). Wolves in Wood Buffalo

National Park (WBNP), Canada, primarily subsist on wood bison (Bison bison) (Carbyn

et al. 1993). In Riding Mountain (Carbyn 1983) and Banff National Parks (Huggard

1993a, b), wolves preferentially prey on elk, even though many other ungulate species are

available to them. Prey can vary spatially and seasonally, presumably as a function of

their presence and availability. Pacific coastal wolves prey predominantly on sitka black-

tailed deer (Odocoileus hemonius); however, they selectively capitalize on runs of fall

salmon (Onchorynchus spp.) (Darimont and Reimchen 2002, Darimont et al. 2003).

Wolves in Ontario and Manitoba substantially increase the amount of beaver eaten during

summer months (Voigt et al 1976, Carbyn and Kingsley, 1979Meleshko 1986).

In western Canada, where multi-prey systems exist, wolves can choose from

combinations of elk, moose, mule deer (Oidocoileus hemonius), white-tailed deer

(Oidecoileus virginianus), bighorn sheep (Ovis Canadensis), mountain goat (Oreamnos

americanus), bison, and caribou. In these systems, elk usually prevail in the winter diet

of wolves (Carbyn 1983, Fuller 1989, Paquet 1992, Huggard 1993 a). A gap of

knowledge remains in the central boreal forest of Saskatchewan where wolf foraging

habits are unknown.

I collected information on wolves and their prey to i) identify major winter prey

items for wolves, ii) test for prey selectivity between years (2002, 2003) under the null

hypothesis of non-selective predation, and iii) examine these results in the context of

foraging theory and risk-avoidance behaviour. Based on optimal diet theory, I predicted

that wolves would mainly prey on elk and deer but would select elk (Carbyn 1983,

Meleshko 1986, Paquet 1992, Huggard 1993 a).

In addition to ecological information, this study addresses important conservation

questions. Predators such as wolves are important in ecosystem stabilization (Fryxell and

34

Lundburg 1994, Post et al. 2000). Describing the ecology of predator-prey systems in

fragmented and unique habitats is important for understanding the behavior of wolves in

human dominated systems. Such information is essential for conservationists interested

in managing wolves that range outside of protected areas.

3.2 Methods

3.2.1 Study area

My study area was in central Saskatchewan (Figure 3.1). Prince Albert National

Park (PANP), the core study area, harbours a wolf population (density of ~2/100km2 -

Paquet and Carbyn 2003, Urton et al. unpublished data) reliant on a multi-prey system.

Predominantly boreal forest, PANP is ~3,875 km2 and is the largest protected wilderness

in Saskatchewan. Thirty percent of the area consists of lakes, streams, marshes, and

bogs. The landscape is formed of steeply sloping eroded escarpments, glacial till plains,

and level plateaus intermixed with sparsely treed peat-lands (Acton et al. 1998).

Dominant vegetation include trembling aspen (Populus tremuloides), white spruce (Picea

glauca), black spruce (Picea mariana), jack pine (Pinus banksiana) and balsam poplar

(Populus balsamifera), as well as a variety of shrub (Alnus spp., Betula spp., Salix spp.)

and under-story species. Potential mammalian prey species for wolves include elk,

white-tailed deer, bison, moose, caribou, beaver (Castor canadensis), snowshoe hare

(Lepus americanus), and smaller rodents (Microtus spp., Peromyscus spp.)(Arseneault

2003). Cultivated land, grazing areas, forest harvesting, road construction, and fur-

trapping lines surround PANP and are common in adjacent provincial lands.

3.2.2 Field and laboratory procedures

I sampled wolf faeces (scats) throughout PANP and surrounding areas (Figure

3.2) during two winters (November to March of 2002 and 2003). I used scat analysis (n

= 378 scats), a non-destructive and cost/time effective method (Ackerman et al. 1984,

Ciucci et al. 1996), to estimate the proportion of different prey species consumed by

wolves. Wolf scats were collected wherever encountered on trails, lakeshores, other

linear features, and by backtracking wolves. GPS location and date were recorded and

samples were stored frozen in plastic bags. These scats were differentiated from those of

35

Canada

Province of Saskatchewan

Figure 3.1: Study area in central Saskatchewan and Prince Albert National Park

36

Figure 3.2. Wolf scat sample locations in central Saskatchewan and PANP (not all

samples are shown).

37

coyote based on associated signs and tracks, scat size (>30 mm in diameter for wolves),

and appearance. Scats that could not be positively identified as wolf scats were excluded

from the analysis.

Laboratory procedures followed Meleshko (1986), Cuicci et al. (1996) and Kohira

and Rexstad (1999). Scats were transferred to aluminum trays and oven-dried for 24

hours at 90°C as a precaution to kill hydatid tapeworms (Echinococcus granulosus, E.

multilocularis) that may be present in scats. Latex gloves and dust masks were worn

during all steps to decrease the risk of pathogen exposure. Sterilized scats were soaked

for 48 hours and washed thoroughly in a fine grid sieve (1 mm) to separate macro- and

microscopic materials. The remaining contents were spread in plastic trays and visually

inspected for prey remains. Remains were identified by comparison with reference hair

samples and with descriptions of hair characteristics. I also examined hairs using a

dissection microscope and compared them with sample photos from Moore et al. (1974)

and Kennedy and Carbyn (1981). I did not attempt to identify plant material.

3.3 Data analysis

3.3.1 Food habits

Prey remains in scats are biased representations of foods consumed by wolves

because of different digestibility and detectability of prey items within scats (Weaver

1993, Kohira and Rextad 1999). I used two indices to describe wolf diets:

occurrence/faeces (OF); the frequency that an item occurs in all scat samples,

and occurrence/item (OI); the item’s frequency among all items identified in all scats

combined. An error rate (15%) was established by blindly re-sampling 10% of the scat

collection for re-identification (Carbyn and Kingsley 1979, Fritts and Mech 1981, Ciucci

et al. 1996). I used ANOVA to test for differences in OF indices between years (Zar

1999).

3.3.2 Biomass consumption

Smaller prey species, having more hair per unit body weight, produce more scats

per unit prey weight consumed, leading to an over-estimation of small prey species in

carnivore diets (Weaver 1993, Ciucci et al.1996). Because of biases inherent in OF and

38

OI indices, frequency of identification of prey remains do not give a representative

picture of the consumed proportion of different prey species when the prey types vary in

size (Floyd et al. 1978, Weaver 1993). A correction factor, developed by Weaver (1993)

from feeding trials with captive wolves, was used to estimate the relative proportion of

biomass of different prey species consumed by wolves in the study area. The equation

used is as follows:

Y = 0.439 + 0.008x (3.1)

Where Y = kg of prey consumed per field collectible scat and x = average weight of an

individual of a particular prey type. Average prey weights were taken from Nowak

(1999). Solving for Y gave an estimation of biomass consumed per collectible scat for

each prey type. Multiplying Y by the number of scats found to contain a particular prey

species gave the relative weights of each prey type consumed. These values were used to

estimate the percent biomass contribution of different prey species to wolf diets and

represent a more biologically meaningful evaluation of diet (Ciucci et al. 1996, Biswas

and Sankar 2002). Biomass estimates excluded non-mammalian prey.

3.3.3 Estimation of prey selectivity

I used the program SCATMAN (Hines and Link 1993; Link and Karanth 1994) to

arrive at the expected proportions of prey species in scat. The observed proportion of

prey species in scats was compared with the expected proportions to determine if

predation by wolves was selective. The expected proportion of scats containing a prey

species was calculated using point estimates of auxiliary variables: scat rate production

per prey species (λi) and prey density (di) (Link & Karanth 1994). The multinomial

likelihood estimator used to compute the expected proportion of scats from a kill of a

particular prey species is:

Πi = diλi (3.2)

Σi diλi

39

where λ is the number of scats produced from a single kill of species i. Estimates

of λ were derived using data from Floyd et al (1978) and Weaver (1993). Individual prey

density estimates (No. per km2 ± SD) were taken from Arseneault (2003); Elk: 0.19 ±

0.25, white-tailed deer: 0.94 ± .18, moose: 0.19 ± 0.22, beaver: 1.0 ± 0.5. Although

density estimates have not been confirmed independently, bootstrapping procedures

within the model should compensate for imprecision.

I compared observed and expected proportions of prey species using the χ2

goodness of fit test (Zar 1999). Because density estimates are inexact and highly

variable, correction factors accounting for over-dispersion in tests of goodness of fit were

applied by using SCATMAN. Variability in the density estimates of each prey species

and number of scats produced from a kill of any prey species is a potential source of

inflation of Type I error (Link and Karantth 1994). The program SCATMAN

incorporates the effect of such variability (Link and Karanth 1994) and reduces the

inflation of Type I error to produce an unbiased probability value. I implemented the

parametric bootstrap procedure of the program for 1000 times to alleviate the above

problem. Patterns in overall prey selection were inspected for use of each species as

calculated by SCATMAN. I chose a level of significance of α < 0.01. A sensitivity

analysis adjusting the coefficient of variation (CV) for scat rate production from 10% to

40% is automatically performed in each run of the model. To be conservative, I chose to

examine statistics generated at CV = 40% (Karanth & Sunquist 1995). I performed this

analysis on data from both years.

3.4 Results

3.4.1 Wolf food habits

Of 426 food items identified in 378 scats, the most common item was white-tailed

deer in both OF and OI indices for combined years (43% and 39%, respectively),

followed by elk (33%, 30%), moose (7%,6%), beaver (5%,4%), snowshoe hare (2%,1%),

avian (2%, 1%), vegetation (4.5%, 4%), and other (5%, 5%); (see Table 3.1). I found no

differences in OF indices between years; see Table 3.2, Figure 3.3 (ANOVA, F = 0.0684,

df = 1, p = 0.804).

40

3.4.2 Biomass contribution

Estimation of the relative biomass contribution of different prey species to wolf

diet gave a clearer assessment of prey use than results obtained from frequency of

occurrence (Weaver 1993). In terms of OF, white-tailed deer contributed more than elk.

However, using relative biomass, elk contributed nearly 50% of available biomass to

winter wolf diet whereas deer contributed only 33%. Moose contributed 14% of

biomass, and smaller mammals such as beaver and snowshoe hare showed a cumulative

biomass contribution of 1.5 % (Table 3.1).

3.4.3 Prey selectivity

A comparison of observed and expected proportions of prey species in wolf scats

based on individual densities rejected the hypothesis of non-selective predation in both

study years (2002: χ2 = 39.6, p = 0.99, df = 3; 2003: χ2 = 145.0, p = 0.99, df = 3).

Because there was evidence of selective predation in the overall pattern of prey use by

wolves in each year, the results of the analyses were examined to infer selectivity for

each species. In 2002, elk (p = 0.0001) and deer (p = 0.002) were found to be consumed

by wolves disproportionate to their availability; i.e. wolves selectively consumed both

species. I found moose and beaver consumed in proportion to their availability (p = 0.46,

p = 0.49 respectively). In 2003, I found elk (p = 0.0001) and beaver (p = 0.0001) were

consumed disproportionately to their availability. Wolves selectively preyed upon elk

but avoided beaver. I found deer and moose to be consumed in proportion to their

availability (p = 0.95, p = 0.04 respectively).

3.5 Discussion

3.5.1 Wolf diet composition

Preference for cervids by wolves was clearly demonstrated by this study. I found elk to

be the most selected prey with the largest winter contribution of biomass in the wolf diet.

Despite the paucity of ecological data in this area, the patterns demonstrated by this

analysis generally agree with findings from studies of wolf food habits in Riding

Mountain National Park, an area similar in habitat features (Carbyn 1983, Meleshko

1986, Paquet 1992). I found that wolves selected elk in the study area in both years. Elk

41

Table 3.1: Composition of wolf scats and relative biomass contribution of different prey

species to winter wolf diet in Prince Albert National Park (2002, 2003)

Species

No. of

scats %OF %OI

Avg. weight

(kg)

% Biomass

contribution

Elk 125 33 30 286 50.48

White-tailed deer 163 43 39 120 33.77

Moose 27 7 6 390 14.23

Beaver 18 5 4 23 1.46

Snowshoe hare 1 0 0 1.5 0.06

Avian 6 2 1 n/a n/a

Vegetation 17 4.5 4 n/a n/a

Other 20 5 5 n/a n/a

No. of feces = 378

No. of items = 426

42

Table 3.2. Composition of wolf scats in each sampling year in Prince Albert National

Park

2002 2003

Species

No. of

scats %OF %OI

No. of

scats %OF %OI

Elk 28 36 34 97 32 29

White-tailed deer 21 27 26 142 47 42

Moose 16 21 20 11 4 3

Beaver 9 12 11 9 3 3

Snowshoe hare 0 0 0 1 0 0

Avian 0 0 0 6 2 2

Vegetation 1 1 1 16 5 5

Other 3 4 4 17 6 5

TOTAL 78 101 96 300 99 89

total items 82 339

43

05

101520253035404550

% F

requ

ency

of O

ccur

renc

e of

Pre

y in

Wol

f Sca

ts

2002 2003Year

ELK

WHITE-TAILED DEER

MOOSEBEAVER

SNOWSHOE HARE

AVIAN

VEGETATIONOTHER

Figure 3.3: Proportions of prey species in winter wolf diets in 2002 and 2003 in PANP,

central Saskatchewan.

44

occur in relatively high densities, contributing to increased encounter rates with

predators. However, their gregarious nature may also be a factor that increases their

chance of depredation (Nelson and Mech 1981, Messier et al. 1988, Huggard 1993a,

1993b, Hebblewhite and Pletscher 2002).

Deer are far more abundant in terms of density than other ungulates in this system

(Arseneault 2003), explaining their standing as the most frequently occurring food item

in wolf scats. However, the relative biomass contributed to wolf diet was nearly half that

of elk. Moose were also found to be an important dietary item for wolves. Although this

ungulate species is available to wolves, behavioural constraints may operate to relax

predation on moose relative to elk and deer. Moose are generally solitary species and

aggressively defend themselves and their young (Mech 1966). Killing the largest bodied

ungulate in this system, with the latter behavioural characteristics, presents high risk for

wolves.

Beaver were a secondary prey item for wolves and were consumed in proportion

to their abundance in 2002 but avoided in 2003. Beavers are inactive above water in

winter months and are not easily available to wolves. Samples collected in spring (March

and April) in 2002 were over-represented leading to the conclusion of non-selection.

Samples collected in 2003 underrepresented spring and therefore describe a strictly

winter diet where wolves would seemingly “avoid” beaver.

The appearance of snowshoe hare and avian prey at low frequencies demonstrates

the wolf’s plasticity to hunt opportunistically. Although these species are abundant and

available, they appear infrequently in wolf diets. Expending energy on such small prey

all the time likely does not pay.

Livestock, primarily cattle (Bovis spp.), occur within and near my study area.

Livestock depredation by wolves is well documented throughout the world (Pullianien

1965, Fritts and Mech 1981, Gunson 1983, Tompa 1983, Vos 2000). No evidence of hair

from cattle was found in 378 wolf scats. Livestock depredation either occurs so

infrequently that this type of analysis could not detect it, or other ungulate species

provide an adequate natural prey base facilitating a disinterest in livestock (Vos 2000).

Moreover, venturing on to agricultural land may present too great a risk as wolves often

have negative encounters with humans (Mazzioli et al 2002).

45

A herd of approximately 300 plains bison also occupies the south-west quadrant

of PANP. I found no evidence of bison in wolf scats in winter. These large bodied and

highly defensive ungulates may present too large a challenge and too great a cost to

wolves. Although wolves that were tracked near bison ranges within the study area were

observed investigating bison bedding and feeding sites, no wolf predation on bison was

documented (L. O’Brodovich personal communication). A summer diet investigation my

indicate that wolves prey on calves. Other natural prey may exist in sufficient abundance

that switching to bison is not required. In areas such as WBNP, wolves predominantly

prey on bison. However, alternate cervid prey are not abundantly or readily available

(Carbyn et al. 1993, Joly and Messier 2000).

3.5.2 Risk-avoidance and optimal foraging

Animals select for a particular food type when the proportion of that type in the

animal’s diet is higher than that in the environment (MacArthur and Pianka 1966,

Stephens and Krebs 1986). To obtain food, predators must expend time and energy, first

in searching for prey and then handling it. Generalist predators such as wolves pursue

both more and less profitable types of prey (Jedrzejewski et al. 2000, Huggard 1993 a,

Mech and Boitani 2003). Optimally foraging predators should balance risk and

profitability to maximize their overall rate of energy intake.

Wolves risk injury (Rausch 1967, Pasitschniak-Arts et al. 1988) and mortality in

attempting to kill large-bodied prey (Mech 1970). Healthy, vigorous prey often escape

wolf predation by fighting or fleeing (Mech 1984, Nelson and Mech 1993, Stephenson

and Van Ballenberghe 1995). Weaver (1992) noted that about 25% of 1,450 wolves

killed by humans in control programs in Alaska showed traumatic skull injuries,

presumably inflicted by moose and other large prey. On occasion, moose, bison, elk, and

deer can kill attacking wolves (Stanwell-Fletcher 1942, Frijlink 1977, Nelson and Mech

1985, Mech and Nelson 1990, Weaver 1992), with risk increasing with size of prey

(Weaver et al. 1992). Several studies have shown that predators often search for less

risky opportunities rather than attack such dangerous prey (Stephens and Krebs 1986: 128

-150, Forbes 1989). Although moose are available in density equal to that of elk, wolves

may avoid preying on them to avoid risk. When prey that flee to avoid predation, such as

46

elk and deer, are present in high densities and portray much lower risk relative to gains,

wolves may prefer to search for and attack them. Bison are not taken at all in this system

presumably because of the energy demand incurred, risk involved in capture and

handling, and the availability of less risky prey. Risk involved in killing cattle may be

viewed as a reduced conflict with humans (Mazzioli et al. 2002).

Further, wolves require a mean rate of 1.7 kg of prey/wolf/day for daily

maintenance requirements and 3.2 kg of prey/wolf/day for successful reproduction (Mech

1970). Moose provide roughly 4.4-6.3 kg/wolf/day (Mech 1966), white-tailed deer

roughly 3.8-2.9 kg/wolf/day (Paquet and Carbyn 2003), and elk falling somewhere

between those ranges based on body mass. Therefore it may not be necessary to risk

injury when adequate prey are available that meet a wolf’s daily metabolic requirements.

My results lend support to the notion that wolves choose to balance risk in

foraging strategies in accordance with the dangerous prey hypothesis (Forbes 1989). We

must note, however, that predation on a prey species is likely to be determined by the

density of groups rather than individuals, which influences encounter rates between the

predator and its prey in a forested ecosystem (Huggard 1993 a, Karanth and Sunquist

1995, Biswas and Sankar 2002). Although density of groups affects encounter rates,

group size would also be an important factor influencing the probability of sighting prey

by the predator. I have data only on individual densities and so must be cautious when

interpreting results. In any case, large profitable prey are readily available in this

environment, yet wolves select for medium sized ungulate prey. Adapted for chase,

wolves expend more energy in the pursuit of these prey, however this cost may be offset

by a considerable decrease in risk of injury or death in the process.

I noted wolves capitalizing on a wide variety of prey types in this system,

indicative of a generalist predator. However, prey selection is evident and is in

concordance with optimal foraging theory where wolves should balance risk and energy

demands with profitability. Social effects on diet, where young wolves observe

experienced conspecifics while hunting and learn to recognize the same search images,

may facilitate this pattern (Shettleworth et al. 1993).

Predators need considerable behavioural plasticity to use effectively prey

assemblages in different ecosystems. Large body size and flexible predatory behaviour

47

release wolves from the constraint of prey specificity. Human activity has modified most

habitats available to wolves leading to changes and perhaps reductions in ungulate

distribution and abundance. Given the rate of habitat loss in the boreal forest (Hobson et

al. 2002, Fitzsimmons 2002), PANP remains a vital area for examining wolf food habits

and the subsequent effects of human-induced change on ecosystem diversity.

48

3.6 References Ackerman, B. B., F. G. Lindzey, and T. P. Hemker. 1984. Cougar food habits in

southern Utah. Journal of Wildlife Management 48: 147-155. Acton D. F., G. A. Padbury and C. T. Stushnoff. 1998. The eco-regions of

Saskatchewan. Canadian Plains Research Centre, Saskatchewan Environment and Resources Management, Regina, Saskatchewan.

Arseneault A. A. 2003. Status and Management of Wildlife in Saskatchewan, 1999- 2001. Saskatchewan Environment Fish and Wildlife Technical Report.

Begon, M, J. L. Harper and C. R. Townsend. 1996. Ecology: individuals, populations and communities. Blackwell Science Ltd. Oxford.

Bekoff, M., T. J. Daniels and J. L. Gittleman. 1984. Life history patterns and comparative social ecology of carnivores. Annual Review of Ecology and Systematics 15: 191-232.

Biswas, S. and K. Sankar. 2002. Prey abundance and food habits of tigers (Panthera tigris tigris) in Pench National Park, Madhya Pradesh, India. Journal of Zoology London 256: 411-420.

Carbyn, L. N. 1983. Wolf predation on elk in Riding Mountain National Park, Manitoba. Journal of Wildlife Management 47: 963-976.

Carbyn, L. N. and M. C. Kingsley. 1979. Summer food habits of wolves with the emphasis on moose in Riding Mountain National Park. Proceedings of the. North American Moose Conference Workshop 15: 349-361.

Carbyn, L. N., S. M. Oosenbrug and D.W. Anions. 1993. Wolves, bison and the dynamics related to the Peace Athabaska Delta in Canada’s Wood Buffalo National Park. Circumpolar Research Series, no. 4. Canadian Circumpolar Institute, University of Alberta, Edmonton, AB, Canada. Chesson, J. 1983. The estimation and analysis of preference and its relationship to foraging models. Ecology 64: 1297-1304. Ciucci, P., L. Boitani, E. R. Pelliccioni, M. Rocco, and, I. Guy. 1996. A comparison of scat-analysis methods to assess the diet of the wolf Canis lupus. Wildlife Biology 2: 37-48. Darimont, C. T. and T. E. Reimchen. 2002. Intra-hair stable isotope analysis implies

seasonal shift to salmon in gray wolf diet. Canadian Journal of Zoology 80: 1638-1642.

49

Darimont, C. T., T. E. Reimchen and P. C. Paquet. 2003. Foraging behaviour by gray wolves on salmon streams in coastal British Columbia. Canadian Journal of Zoology 81: 349-353.

Fitzsimmons, M. 2002. Estimated rates of deforestation in two boreal landscapes in

central Saskatchewan, Canada. Canadian Journal of Forest Research 32: 843-951.

Floyd, T. J., L. D. Mech and P. J. Jordan. 1978. Relation wolf scat contents to prey

consumed. Journal of Wildlife Management 42:528-532.

Forbes, L. S. 1989. Prey defences and predator handling behavior: the dangerous prey hypothesis. Oikos 55: 155-158.

Frijlink, J. H. 1977. Patterns of wolf pack movements prior to kills as read from tracks in Algonquin Park, Ontario, Canada. Bijdragen Tot De Dierkunde 47:131-137.

Fritts, S. H. and L. D. Mech. 1981. Dynamics, movements and feeding ecology of a newly protected wolf population in northwestern Minnesota. Wildlife Monographs 80: 1-79.

Fryxell, J. M. and P. Lundberg. 1994. Diet choice and predator-prey dynamics. Evolutionary Ecology 8: 407-421.

Fuller, T. K., and L. B. Keith. 1980. Wolf population dynamics and prey relationships in northeastern Alberta. Journal of Wildlife Management 44:583-602.

Fuller, T. K. 1989. Population dynamics of wolves in north-central Minnesota. Wildlife Monographs No. 105.

Gunson, J. R. 1983. Wolf predation of livestock in western Canada. Pages 102-105 in

L. N. Carbyn, editor. Wolves in Canada and Alaska. Canadian Wildlife Service Report Number 45.

Hebblewhite, M. and D. H. Pletscher. 2002. Effects of elk group size on predation by

wolves. Canadian Journal of Zoology 80: 800-809. Hobson, K. A., E. M. Bayne and S. L. Van Wilgenburg. 2002. Large-scale conversion

of forest to agriculture in the Boreal Plains of Saskatchewan. Conservation Biology 16: 1530-1541. Huggard, D. J. 1993a. Prey selectivity in Banff National Park. I. Prey species.

Canadian Journal of Zoology 71: 130-139.

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Huggard, D. J. 1993b. Prey selectivity of wolves in Banff National Park, II. Age, sex, and condition of elk. Canadian Journal of Zoology 71:140-147.

Jedrzejewski, W., K. Schmidt, J. Theuerkauf, B. Jedrzejewska, and H. Okarma. 2000. Prey selection and predation by wolves in Biolwieza Primeval Forest, Poland. Journal of Mammalogy 81: 197-212. Joly, D. O. and F. Messier. 2000. A numerical response of wolves to bison abundance

in Wood Buffalo National Park, Canada. Canadian Journal of Zoology 78: 1101-1104.

Karanth, K. U. and M. E. Sunquist. 1995. Prey selection by Tiger, leopard and dhole in tropical forests. Journal of Animal Ecology 64:439-450. Kennedy, A. J. and L. N. Carbyn. 1981. Identification of wolf prey using hair and feather remains with special reference to western Canadian National Parks.

Unpublished Canadian Wildlife Service Report. Kie, J. G. 1999. Optimal foraging and risk of predation: effects on behaviour and social structure in ungulates. Journal of Mammalogy 80: 1114-1129. Kohira, M. and E. A. Rextad. 1997. Diets of wolves, Canis lupus, in logged and

unlogged forests of southeastern Alaska. Canadian Field Naturalist. 111: 429-435.

Kotler, B. P., J. S. Brown and W. A. Mitchell. 1994. The role of predation in shaping

the behaviour and community organization of desert rodents. Australian Journal of Zoology 42: 449-466. Krebs, J. R. 1978. Optimal foraging: decision rules for predators. Pages 23-63 in. J. R Krebs and N. B. Davies, editors. Behavioural Ecology. Sinauer, Sunderland, MA. Krebs, J.R., D.W. Stephens and W.J. Sutherland. 1983. Perspective in optimal foraging. Pages 165-216 in A. H. Brush and G. A. Clark, Jr., editors. Cambridge University Press, New York, USA. Link, W. A. and K. U. Karanth. 1994. Correcting for overdispersion in tests of prey

selectivity. Ecology 75: 2456-2459. MacArthur, R. H. and E. R. Pianka. 1966. On optimal use of a patchy environment. American Naturalist 100: 603-609. Maziolli, M., M. E. Graipel and N. Dunstone. 2002. Mountain lion depredation in southern Brazil. Biological Conservation 105: 43-51. Mech, L. D. 1970. The wolf: the ecology and behaviour of an endangered species. Doubleday/Natural History, Garden City, NY, USA.

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Mech, L .D. 1966. The Wolves of Isle Royale. U.S. National Park Service Fauna Series, no. 7. Unite States Government Printing Office. 210 pages.

Mech, L.D. 1984. Predators and predation. Pp.189-200 in L.K. Halls, editor. White

tailed deer ecology and management. Stackpole, Harrisburg, PA.

Mech, L. D. and L. Boitani. 2003. Wolves: Behavior, Ecology and Conservation. University of Chicago Press. Chicago and London.

Mech, L. D., and M. E. Nelson. 1990. Evidence of prey-caused mortality in three wolves. American Midland Naturalist 123:207-208.

Meleshko, D. W. 1986. Feeding habits of sympatric canids in an area of moderate ungulate density. M.Sc. Thesis. University of Alberta, Edmonton, AB.

Messier, F. 1985a. Social organization, special distribution, and population density of

wolves in relation to moose density. Canadian Journal of Zoology 63: 239-245.

Messier, F. 1985b. Solitary living and extraterritorial movements of wolves in relation to social status and prey abundance. Canadian Journal of Zoology 63:239-245.

Messier, F and M. Crete. 1985. Moose-wolf dynamics and the natural regulation of moose populations. Oecologia 65: 503-512.

Messier, F. J. Huot, D. Le Hanaff, and S. Luttich. 1988. Demography of the George River Caribou Herd: Evidence of population regulation by forage exploitation and range expansion. Arctic 41: 279-287.

Miller, F.L. 1995. Status of wolves in the Canadian Arctic islands. Pages. 35-42 in L. N. carbyn, S. H. Fritts and D. R. Seip, editors. Ecology and conservation of wolves in a changing world. Canadian Circumpolar Institute, Edmonton, AB.

Moore, T. D., L. E. Spence and C. E. Dugnolle. 1974. Identification of the dorsal guard hairs of some mammals of Wyoming. Wyoming Game and Fish Deptartment, Cheyenne, WY. 177 pages.

Nelson, M. E., and L. D. Mech. 1981. Deer social organization and wolf predation in northeastern Minnesota. Wildlife Monograph 77.

Nelson, M. E., and L. D. Mech. 1985. Observations of a wolf killed by a deer. Journal of Mammalogy 66:187-188.

52

Nelson, M. E. and L.D. Mech. 1993. Prey escaping wolves, Canis lupus, despite close proximity. Canadian Field Naturalist 107: 245-246.

Nowak, R. M. 1999. Walker's Mammals of the World, Vol. I. Johns Hopkins University Press, Baltimore, USA.

Paquet P. C. 1992. Prey use strategies of sympatric wolves and coyotes in Riding Mountain National Park, Manitoba. Journal of Mammalogy 73: 337-343. Paquet, P. C. and L. N. Carbyn. 2003. Wolf, Canis lupus. Wild mammals of North

America: biology, management, and conservation. In G. A. Feldhammer, B. C. Thompson and J. A. Chapman. Johns Hopkins University Press. Baltimore, MD, USA.

Pasitschniak-Arts, M., M. E. Taylor, and L. D. Mech. 1988. Skeletal injuries in an adult

arctic wolf. Arctic Alpine Research 20:360-365. Peterson, R. O. and R. E. Page. 1988. The rise and fall of the Isle Royale Wolves.

Journal of Mammalogy 69:89-99. Post, D.M., M.E. Conners, and D.S. Goldberg. 2000. Prey preference by a top predator

and the stability of linked food chains. Ecology 81:8-14. Poulle, M. L., L. Carles, B. Lequette. 1997. Significance of ungulates in the diet of

recently settled wolves in the Mercantour Mountains (Southeastern France). Review of Ecology (Terr. Vie) 52: 357-368.

Pullianinen, E. 1965. Studies on the wolf (Canis lupus) in Finland. Annals of Zoologica Fenniscandia 2: 215-259. Rausch, R. A. 1967. Some aspects of the population ecology of wolves, Alaska.

American Zoologist 7: 253-265.

Shettleworth, S. J., P. Reid and M. S. Plowright. 1993. The psychology of diet selection. Pages 56-77 in R.N. Hughes, editor. Diet Selection. Blackwell Scientific Publications, Oxford, UK.

Sih, A. 1993. Effects of ecological interactions on forager diets: competition, predation risk, parasitism and prey behaviour. Pages 182-211 in R.N. Hughes, editor. Diet Selection. Blackwell Scientific Publications, Oxford, UK.

Stanwell-Fletcher, J. F. 1942. Three years in the wolves’ wilderness. Natural History 49: 1-7.

Stephens, D. W., and J. R. Krebs. 1986. Foraging theory. Princeton University Press, Princeton, New Jersey, USA.

53

Stephenson, T. R. and V. Van Ballenberghe. 1995. Defense of one twin calf against wolves, Canis lupus, by a female moose, Alces alces. Canadian Field Naturalist 109: 251-253.

Terborgh, J., J. Estes, P. Paquet, K. Ralls, D. Boyd-Heger, B. Miller, and R. Noss. 1999. The role of top carnivores in regulating terrestrial ecosystems. Wild Earth 9: 42-56. Terborgh, J., L. Lopez, V. P. Nunez, M. Rao, G. Shahabuddin, G. Orihuela, M. Riveros, R. Ascanio, G. H. Adler, T. D. Lamber, and L. Balbas. 2001. Ecological meltdown in predator-free forest fragments. Science 294: 1923-1926. Tompa, F. S. 1983. Problem of wolf management in British Columbia: conflict and

program evaluation. Pages 112-119 in L. N. Carbyn, editor. Wolves in Canada and Alaska. Canadian Wildlife Service Report Number 45.

Voigt, D. R., G. B. Kolenosky and D. H. Pimlott. 1976. Changes in summer foods of

wolves in central Ontario. Journal of Wildlife Management 40: 663-668. Vos. J. 2000. Food habits and livestock depredation of two Iberian wolf packs (Canis lupus signatus) in the north of Portugal. Journal of Zoology London 251: 457-462. Weaver, J. L. 1992. Two wolves, Canis lupus, killed by a moose, Alces alces, in Jasper

National Park, Alberta. Canadian Field Naturalist 106:126-127. Weaver, J. L. 1993. Refining the equation for interpreting prey occurrence in gray wolf

scats. Journal of Wildlife Management 57: 534-538. Zar, J.H. 1999. Biostatistical Analyis. 4th edition. Prentice Hall, Upper Saddle River,

NJ, USA. Vos. J. 2000. Food habits and livestock depredation of two Iberian wolf packs (Canis lupus signatus) in the north of Portugal. Journal of Zoology London 251: 457-462.

54

4. 0 GREY WOLF TROPHIC RELATIONSHIPS IN THE BOREAL

FOREST: INSIGHTS FROM STABLE ISOTOPES

4.1 Introduction

Describing and understanding patterns and functional relationships between

predator communities and their prey are fundamental objectives for ecologists (Hall and

Raffaelli 1997, Post 2002). Insights into trophic relationships among organisms can help

define species’ roles in communities and track energy flow (Post 2002). Understanding

trophic interactions, community composition, and dynamics of energy flow is also vital

for investigating species' resource requirements and determining how communities, and

ultimately ecosystems, are regulated (Litvaitis 2001).

The Boreal forest ecosystem harbours a diversity of mammalian species. The

grey wolf, a summit carnivore, limits and often regulates its prey on the landscape

(Messier 1994, Terborgh et al. 1999). Carnivores such as wolves exert a strong top-down

role stabilizing the trophic structure of ecosystems (Terborgh et al. 1999, Post et al. 2000,

Wilmers et al. 2003). Wolf kills are important sources of food for avian and mammalian

scavengers (Wilmers et al. 2003).

Within species groups, variance in trophic position or diet among individuals has

several ecological, evolutionary, and conservation implications (Bolnick et al. 2003).

Since natural selection acts on individuals, differences among individuals in diet can

contribute to differences in individual fitness. To date, the tendency has been to describe

diets of animals at the population level with little consideration of individual differences

(Carbyn 1983, Meleshko 1985, Paquet 1992, Durell 2000, Bolnick et al. 2003).

Conservation strategies, in particular, are traditionally based on identifying average

resource requirements for a population (Durell 2000, Bolnick et al. 2003). Differential

foraging strategies, such as those occurring between the sexes, may confer an advantage

in fitness to each group (Casaux et al. 2001, Durell et al. 1993, Beier 1988). However,

individual variability in foraging strategies may also be detrimental to some individuals

in the population, especially those with niches divergent from those that are more

55

common. In carnivores, such as wolves, kin selected traits related to social grouping may

act to counter differential access of individuals to available prey (Creel and Creel 1995,

Creel 1997).

Previously, our understanding of animal trophic relationships has been hampered

by limitations in methodology. Several studies have described the advantages to using

the stable isotope approach to augment more traditional dietary methods (Hobson et al.

1994; Hobson et al. 2000, Litvaitis 2001). Stable isotope assays provide a continuous

measure of trophic position and can potentially identify relatively complex trophic

strategies such as omnivory (Peterson and Fry 1987, Cabana and Rasmussen 1996).

Moreover, they offer discrete information about individuals. Stable nitrogen isotope

ratios show a stepwise enrichment of about 3-5 ‰ in δ15N with increasing trophic level

(DeNiro and Epstien 1978, 1981; Tieszen et al. 1983, Bocherens and Drucker 2003)

whereas δ13C values change little as carbon moves through food webs (Peterson and Fry

1987, France and Peters 1997, Post 2002). However, when δ13C values of sources differ,

they can be used to evaluate the ultimate sources of carbon for a consumer (Tieszen et al.

1983). Coupled δ15N and δ13C values provide both source and trophic information

(Schell and Ziemann 1989, Hobson and Clark 1993, Hobson et al. 1994). Isotopic

measurements of metabolically active tissues like blood plasma, whole blood, and muscle

represent integrations of diet over days to several weeks, while bone collagen can reflect

diet over a lifetime (Hobson et al. 1993). Metabolically inactive tissue, such as hair,

reflects diet during the growth phase and so represents diet up to many months (Tieszen

et al. 1983, Darimont and Reimchen 2002).

Trophic relationships and feeding ecology among seabirds (Hobson et al. 1994),

black bears (Ursus americanus), grizzly bears (Ursus arctos) (Hilderbrand et al. 1996,

Jacoby et al. 1999, Hobson et al. 2000), and more recently, grey wolves (Szepanski et al.

1999, Darimont & Reimchen 2002, Bocherens & Drucker 2003) have been studied with

good success by measuring stable isotope ratios in feathers and hair. However, these

studies primarily trace marine dietary inputs, which are easily differentiated isotopically

from terrestrial inputs (Chisholm et al. 1982, Hobson and Sealy 1991, Ben-David et al.

1997a, 1997b). Past applications of stable isotopes used distinctive δ13C and δ15N values

of various food sources to determine their relative contribution to an animal’s diet

56

(Hobson et al. 1994; Ben-David et al. 1997a, 1997b,). This method relies on mass

balance equations and distinct isotope signatures of various sources to determine their

relative contributions to a mixed signature and is limited to solving for n+1 sources when

n isotopes are used. However, recently developed models (Phillips and Gregg 2003)

have since allowed the examination of ranges of possible dietary inputs in complex

systems in which the number of isotopically different sources exceeds the number of

isotopes used.

In this study, I applied conventional (faecal analysis) and stable isotope

techniques to elucidate trophic relationships involving wolves in the boreal forest of

central Saskatchewan, Canada. I was particularly interested in examining variance in

isotopic signatures among individual wolves. I predicted that wolves would show among

the highest δ15N values for all mammals (i.e. corresponding to their assumed top

predatory role) and that isotopic variance among individuals would be relatively low for

these social carnivores versus other more solitary species.

4.2 Methods

4.2.1 Study area Wolves inhabit nearly two thirds of the boreal forest of Saskatchewan from Duck

Lake, (52°81 N, 106°22 W) in the south to the North West Territories in the north. This

region, the Boreal Plain Eco-zone, is a forested matrix with interspersed wetlands, lakes

and rivers (Acton et al. 1998). Nearly 30% of this area is covered with water. The

landscape consists of steeply sloping eroded escarpments, glacial till plains, and level

plateaus intermixed with sparsely treed peat-lands (Acton et al. 1998). My study area, in

the central region of Saskatchewan (Figure 2.2 in Chapter 2), encompasses the Aspen

Parkland and Boreal Forest Transition eco-regions and has a complex mixture of

communities (Acton et al. 1998). Dominant vegetation includes trembling aspen

(Populus tremuloides), white spruce (Picea glauca), black spruce (Picea mariana), jack

pine (Pinus banksiana), and balsam poplar (Populus balsamifera), as well as a variety of

shrub (Alnus spp., Betula spp., Salix spp.) and under-story species. Potential mammalian

prey species of wolves include elk (Cervus elaphus), white-tailed deer (Oidecoileus

57

virginianus), bison (Bison bison), moose (Alces alces), caribou (Rangifer tarandus),

beaver (Castor canadensis), snowshoe hare (Lepus americanus) and small rodents

(Microtus, Peromyscus, etc.) (Arsenseault 2003). Cultivated land, grazing areas, forest

harvesting, road construction, and fur-trapping lines are prominent in this area. This

region accounts for the bulk of the province’s merchantable timber. Prince Albert

National Park (PANP) comprised the core of my study area.

4.2.2 Sample collection

During the winters (Nov-Mar) of 2001/2002 and 2002/2003, I opportunistically

collected hair samples from 18 mammalian species [wolf, coyote (Canis latrans), red fox

(Vulpes vulpes), black bear, lynx (Lynx canadensis), wolverine (Gulo gulo), fisher

(Martes pennanti), marten (Martes americana) raccoon (Procyon lotor), elk, white-tailed

deer, bison, moose, caribou, beaver, snowshoe hare, northern flying squirrel (Glaucomys

sabrinus), and muskrat (Ondatra zibethica)] from trappers. Some hair samples were also

obtained from road kills.

Each sample consisted of guard hairs. All species analysed were killed in winter,

therefore, I assumed hairs taken were probably grown from mid-summer to late-fall or

early-winter; roughly 4 to 5 months. Sex, age, and body condition were unknown for

most samples.

4.2.3 Isotopic analyses Hair samples were cleaned with a 2:1 chloroform:methanol solution and dried for

24 hours under a fume hood. One mg sub-samples were loaded into 4x6 mm tin cups

(Isomass Scientific) and combusted at 1,800 °C in a Robo-Prep elemental analyzer.

Further analyses of resultant CO2 and N2 gases were performed using an interfaced

Europa 20:20 continuous-flow isotope ratio mass spectrometer (CFIRMS). Isotope

signatures are expressed in delta notation as ratios relative to PeeDee Belemnite (PDB)

(carbon) and atmospheric N2 (Air)(nitrogen) standards. Analytical error was estimated to

be ±0.1‰ for δ13C and ±0.3‰ for δ15N values based on thousands of measurements of an

albumen lab standard.

58

4.2.4 Isotopic calculations

I used Kruskal-Wallis tests to evaluate differences in isotopic values among

individual wolves in three locations within and outside PANP for carbon and nitrogen

(Zar 1999). I used Dunn’s tests to determine which locations were different and

Levene’s test for homogeneity to examine equality of group variances. Finally, I used Z-

tests on independent proportions with an applied Yates correction to test differences

between measures of diet (Zar 1999). All tests were performed using SigmaStat (2.0).

4.2.5 IsoSource Dietary mixing models provide an indication of relative prey consumption.

Consumer diet-hair discrimination values, although not established for wolves, were

based on captive experiments using red foxes (Roth and Hobson 2000), the closest

relative to the wolf for which values have been measured. These values were ±3.4‰

and ±2.6‰ for δ15N and δ13C values, respectively, and similar to those found for other

mammalian species (DeNiro and Epstien 1981, Kelly 2000). To calculate dietary

endpoints of prey items, I used the mean isotopic signatures of hair corrected for dietary

fractionation by applying the appropriate fractionation values for each isotope.

Measurement error and sample variability of both sources and mixtures combine

to create uncertainty in source contribution estimates. These sources of uncertainty can be

implicitly incorporated by the choice of tolerance values for isotopic mass balance

(Phillips & Gregg 2003). I adjusted mass balance tolerance and source increment values

in IsoSource to 0.1‰ and 1%, respectively.

4.3. Results

4.3.1 Mammalian trophic relationships

As expected, δ13C values for wolves in the central boreal forest of Saskatchewan

reflected a terrestrial C3 dominated ecosystem (Figure 4.2). Table 4.1 summarizes mean

isotopic signatures and their variances for all 18 species examined. The ranges in isotope

values for wolves were broad; 5.4‰ to 11.2‰ for δ15N and -19.7‰ to -24.3‰ for δ13C

suggesting a highly varied diet and dietary differences among individuals during the

period of hair growth. I plotted mean δ15 N and δ13C values for three groups of wolves

(Figure 4.2, Table 4.2) to illustrate this variability: those from within a protected area,

59

Prince Albert National Park (PANP), those outside PANP but at roughly the same

latitude, and those 150 km north of PANP, near Lac La Ronge, SK. I found significant

differences in isotope values for wolves among sites (δ15 N: χ2= 11.6, df = 2, p = 0.003;

δ13C: χ2 = 10.8, df = 2, p = 0.005). La Ronge and Outside wolves were significantly

different from PANP wolves in δ15 N values (Dunn’s test, Q = 3.25, p < 0.05; Q = 2.5, p

< 0.05) and PANP and Outside wolves differed from La Ronge wolves in δ13C values

(Dunn’s test, Q = 3.0, p < 0.05; Q = 2.6, p < 0.05). Variances among the 3 groups were

not equal for either isotope group. Wolves from La Ronge and outside PANP were more

variable than those sampled inside (Levene’s test, F2,44 = 4, p = 0.025 for δ15 N, F2,44 =

7.9, p = 0.001 for δ13C)(Figure 4.2).

Range and standard deviation of the mean stable isotope values for all wolves

were similar to those for coyotes, a solitary generalist carnivore (Table 4.1). Variances

were also similar to those for black bears, an omnivorous species known to have a highly

varied diet (Nowak 1999).

4.3.2 IsoSource

Using a priori knowledge of wolf diets and knowing the prey composition in my

study area based on literature, I selected 5 most likely prey species to include in the

IsoSource model. These were: elk, deer, moose, beaver, and snowshoe hare. I limited

my consumer value to the mean calculated for wolves in and around PANP (n = 26, mean

= 7.3 ‰). The ranges of feasible solutions of the distribution of relative contributions of

each prey item to wolf diet produced by IsoSource are reported as 25th-75th percentile

ranges. These are less influenced by outliers on the tails of the distributions than

minimum and maximum values.

60

δ13C (‰)

-27 -26 -25 -24 -23 -22 -21 -20

δ15N

(‰)

0

2

4

6

8

10

12

Coyote

Red Fox

Wolverine

Raccoon

Snowshoe hare

Beaver

MooseDeer

Elk

Bison

Lynx

Flying squirrel

Wolf

Marten

Caribou Black bear

Fisher

Figure 4.1. Distribution of mean δ13C and δ15N ± SE values (‰) for 18 boreal forest mammals sampled in central Saskatchewan sampled over two consecutive winters (2002, 2003). Values for muskrat are not included in this plot.

61

Table 4.1. Summary of means, standard deviations (SD) and ranges (min to max) for δ15N and δ13C values in hair for 18 mammalian species sampled from the central boreal forest of Saskatchewan.

Species n δ15N (‰) SD range (‰) δ13C (‰) SD range (‰)

Wolf 47 7.4 1.22 5.4 to 11.2 -22.4 1.1 -24.3 to -19.7

Coyote 42 9 1.04 7.1 to 12.2 -23.2 0.7 -24.4 to -21.7

Red fox 9 9.5 1.3 7.3 to 11 -22.4 1.3 -23.6 to -18.7

Black bear 8 6.4 1.9 4.6 to 10.9 -22.7 0.53 -23.7 to -22.0

Marten 3 5.8 0.18 5.6 to 5.9 -22.3 0.3 -22.7 to -22.0

Raccoon 2 8.3 … … -24.3 … …

Fisher 5 6.3 0.75 5.2 to 7.1 -22.9 0.5 -23.4 to -22.3

Wolverine 1 7.3 … … -24.2 … …

Lynx 3 6.1 0.93 5.2 to 7.1 -24.2 1.2 -25.6 to -23 .5

Beaver 8 3.1 1.5 0.5 to 4.9 -24 1.3 -26.3 to -21.8

Moose 7 3.1 0.83 2.0 to 4.4 -25.6 0.5 -26.1 to -23.5

Bison 9 6.2 0.78 5.1 to 7.8 -25.5 1 -26.6 to -23.5

White tailed deer 10 4.3 1.1 2.7 to 6.5 -25.1 1.2 -26.3 to -22.1

Caribou 26 6.8 0.77 5.6 to 8.4 -23.4 0.3 -24.0 to -22.6

Flying squirrel 1 5.5 … … -21 … …

Elk 9 4.8 1.3 2.2 to 6.8 -25.6 1.1 -27.1 to -24.2

Snowshoe hare 5 2.1 2 0.0 to 4.6 -25.8 0.9 -27.3 to -25.1

Muskrat 1 4.3 … … -12.1 … …

62

Table 4.2. Summary of means, standard deviations (SD) and ranges (min to max) for δ15N and δ13C values for 3 groups of wolves sampled at different locations throughout the central boreal forest of Saskatchewan.

Location n δ15N (‰) SD range (‰) δ13C (‰) SD range (‰)

PANP 16 6.5 0.56 5.4 to 8.2 -22.9 0.32 -23.7 to -22.5

Outside PANP 14 7.4 0.98 5.4 to 9.3 -22.5 1.2 -24.3 to -20.4

La Ronge 17 7.9 1.5 5.5 to 11.2 -21.7 1.25 -23.5 to -19.7

63

-25 -24 -23 -22 -21 -20 -19d13C (‰)

5

6

7

8

9

10

11

12

d15N

(‰)

Inside PANPLa RongeOutside PANP

δ13C (‰)

δ15 N

(‰)

Figure 4.2. δ13C and δ15N values and ellipses showing one standard deviation for 3 groups of wolves sampled at different locations throughout the boreal forest of central Saskatchewan, Canada.

64

As solutions, Phillips and Gregg (2003) caution against using a single proportion

such as the mean relative contribution to diet because it misrepresents the actual lack of

uniqueness of the results of the model. I report the mean with my ranges only for

comparative purposes especially when considering the results of my scat analysis.

Wolves relied significantly on elk (range: 30-62%; mean: 48%), white tailed deer (11-

35%; 22%), moose (7-27%; 14%), beaver (4-17%; 8%) and snowshoe hare (4-16%; 8%)

(Figure 4.4).

I compared results from this isotope model with results from a faecal analysis

performed blind to the isotope analyses (Urton et al., in prep.). Percent dietary biomass

consumption, calculated using a regression equation specific to wolf diets (Weaver

1993), did not vary significantly from the mean estimates of relative prey contributions

indicated by IsoSource (elk: z = -0.229, p = 0.819; deer: z = 0.436, p = 0.663; moose: z =

0.220, p=0.826; beaver: z = -0.211, p = 0.833; snowshoe hare: z = -1.91, p = 0.056). This

measure of diet is most meaningful and reflects the relative importance of prey in diet

(Ciucci et al. 1996, Weaver 1993; Scott and Shackleton 1980).

4.4 Discussion

4.4.1 Trophic relationships involving wolves

Carnivores separated into discrete isotopic groups in this boreal forest system. I

initially expected wolves to be at the top of the food chain. Notably, however, red fox

and coyote were more enriched in δ15N and δ 13C. Plant food items originating from

cultivated areas may be enriched in 15N relative to those from forested areas (Riga 1971,

Hobson et al. 1999, Ostrom et al. 2002, Lavin et al. 2003). As scavenging species and

opportunistic hunters of small rodent prey, red fox and coyote may have foraged at or

near the agricultural areas interspersed in the southern boreal landscape. Although,

preying predominantly on large ungulate prey, wolves also prey on smaller mammals

(beaver and snowshoe hare) but tend to avoid open agricultural areas (Carbyn 1983;

Meleshko 1985; Paquet 1992) therefore consuming foods more depleted in 15N than

coyote and red fox. Fisher and marten, opportunists and scavengers, also prey on

medium-to-small sized rodents; however, they rarely venture into open agricultural areas

and are restricted to dense forest cover (Nowak 1999).

65

δ13C (‰)

-23.4 -23.2 -23.0 -22.8 -22.6 -22.4 -22.2 -22.0 -21.8 -21.6 -21.4 -21.2

δ15N

(‰)

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

Wolf

Elk

White-tailed deer

Snowshoe Hare

Moose

(30%-62%)

(11%-35%)

(4% - 17%)

(4% - 16%)

(7% - 27%)

Beaver

Figure 4.3. Mixing polygon for δ13C and δ15N values of five food sources for gray wolves in central Saskatchewan. I report the 25th to 75th percentile ranges for the calculated feasible distributions in IsoSource.

66

As specialists on snowshoe hare, lynx δ13C and δ15N values reflected their

preference for consuming this prey item. They, like snowshoe hare, were the most

depleted in both isotopes (excluding δ13C values for marten) and clearly occupied a

different niche than predatory generalists like wolves, fishers, martens, red foxes or

coyotes. Black bears, an omnivorous species, showed high variation in δ15N values and

were positioned below other strictly carnivorous predators. High variation in δ15N values

indicates a highly varied diet. Black bears are known to forage extensively on forbs,

berries and seeds, but also prey on insects, young of year ungulates, and frequently

scavenge carcasses (Nowak 1999).

Due to dietary overlap, I could not differentiate between δ15N and δ13C values for

elk and deer. Both species browse and graze to the same extent on similar vegetation

types (Nowak 1999). Moose and beaver, although sharing identical δ15N values, differed

in δ13C values (i.e. by 2 ‰). Both species spend much of their time foraging in aquatic

habitat and wetlands, however, beaver spend considerably more time in the water and

also consume the woody portions of plants like willow and aspen to a greater extent.

δ15N values in moose were lower than other ungulates possibly because of the high

proportion of browse in their diet (Sponheimer et al. 2003, Drucker et al. 2003). Bison

were enriched in 15N reflecting their preference for sedges and grasses around wetlands

(Nowak 1999, Stewart et al. 2003). Also, bison are foregut fermenters and digest large

amounts of symbiotic microflora and this can result in 15N enrichment (Sponheimer et al

2003). Caribou were even more enriched in δ15N and δ13C than bison and other

herbivores, likely reflecting their preference for lichens (Barnett 1994; Nowak 1999;

Wison and Ruff 1999, Drucker et al. 2001). Lichens, a water-stressed species, show

higher δ13C values than other plant forms (Barnett 1994). Similarly, I speculate that

flying squirrels showed this enrichment feeding on lichens, mushrooms, and bracket

fungi (Wilson and Ruff 1999); however, isotope values for fungi are unknown.

4.4.2 Individual variation

I expected similar isotopic values among individual wolves because of their social

foraging behaviour (Mech 1970). Wolves hunt cooperatively and I assumed that all

individuals have access to the same food resources as indicated by conventional diet

67

studies (Scott and Shackleton 1980, Meleshko 1986, Carbyn and Kingsley 1979, Carbyn

et al. 1985, Paquet 1992, Weaver 1993, Ciucci et al. 1996). However, the variability in

isotopic signatures were similar to those measured for coyotes and bears, solitary

generalists known to have highly varied diets.

Stable isotope values of wolves sampled outside of PANP and La Ronge were

significantly different and more variable from those sampled within PANP. Differences

in prey abundance and distribution may account for these differences indicating varied

wolf diets. Exploited wolf populations (e.g. those occurring outside of PANP)

experience higher population turnover and pack instability compared to protected wolves

(Lehman et al. 1992). Increased movements of individuals among packs can also

contribute to greater population genetic variation (Lehman et al. 1992, Meier et al. 1995).

Enhanced movements may also be linked to isotopic variation. Unstable packs and lone

individuals often have larger home ranges and are more mobile than established stable

packs (Mech and Boitani 2003). A more varied diet may result as individual wolves can

specialize on different prey or consume a larger variety of prey compared with animals in

social groups. Our findings of higher isotopic variation within the two exploited wolf

populations compared with the protected wolves of PANP are consistent with this

hypothesis.

High individual isotopic variation in animals has been demonstrated to be due to

consumption of a variety of marine resources and/or vegetation (Ben-David 1997,

Hilderbrand et al. 1996, Szepanski 1999, Darimont and Reimchen 2002). For example,

Szepanski et al. (1999) found that wolves on the coast of SE Alaska had variable isotopic

signatures due to differential consumption of marine-derived salmon resources. They also

found that interior Alaska wolves, in a system where individuals have limited access to

salmon and abundant access to caribou, showed little isotopic variation among

individuals. In contrast I found considerable isotopic variation among individuals in a

completely terrestrial C3 system with no access to extremely different isotopic prey such

as marine resources. High intraspecific variation in isotope ratios indicates large and

consistent diet variation (Bolnick et al. 2003). Given the high prey diversity in the boreal

forest, individual dietary specialization is possible but was unexpected due to the

probable predominance of social foraging.

68

Bolnick et al. (2003) demonstrate that between-individual variation in resource

use can constitute a large portion of a population's dietary niche width. Individual

specialization can result from stochastic events, patchy environments, or behavioural

variation among individuals (Bolnick et al. 2003). These short- and long-term processes

may cause individuals to use different resources if they have different preferences or

resource use efficiencies, reflecting variable morphological, behavioural, or physiological

capacities to handle alternative resources (Bolnick et al. 2003). Diets of wolves are often

generally examined using information based on kills or faecal remains. These techniques

cannot readily differentiate lone individuals from those belonging to packs. The stable

isotope data, however, give individual dietary history.

Wolves, when belonging to packs, often hunt cooperatively, but not all pack

members necessarily have access to the same resources from a kill. Depending on age

and social structure of a pack, each individual may not have the same access to all parts

of the carcass or any access at all. Also, participation in packs may change seasonally

and this may or may not overlap with the period of hair growth (Mech 1970). As obligate

predators and opportunists, wolves eat small mammals, fish and birds when and where

available (Darimont et al. 2004). Lower ranking individuals who cannot support their

metabolic requirements on the left-overs of what their pack kills may also subsist in this

manner. Lone individuals may also rely more on these types of prey items.

Wolves exhibit strong territoriality (Mech 1970, Mech and Boitani 2003). In

patchy environments where soil types, vegetation types, and even prey type and

distribution vary across the landscape, wolves occupying different territories may

consume different prey resulting in isotope signature variation. Stable isotope signatures

in terrestrial C3 food webs have been shown to vary among biomes due to differing

biogeochemical processes (Cormie and Schwarcz 1994) and anthropogenic influences on

the landscape (Lavin et al. 2002). Wolves foraging in multiple biomes may express

different isotopic signatures relative to those wolves restricted to one biome throughout

their lives.

Black bear and mustelid remains have been found in wolf scats (Kohira 1997,

White et al. 2002) and the incidence of wolves preying on black bears has been

documented in British Columbia, Manitoba and Minnesota (Horesji et al. 1984; Paquet

69

and Carbyn 1986; Rogers and Mech 1988, Darimont et al. 2004). It is plausible that

wolves may have access to discarded black bear carcasses from hunting outfitters and

baiting operations in our study area (Paquet, Giroux, Archer, personal communications).

Garbage in rural areas is also potentially accessible to wolves. Nutritional stress may

further account for individual isotopic variation (Hobson et al. 1993).

4.4.3 Dietary mixing models

My derived mixing polygon in Isosource was broad with wolf mean isotopic

values (the “mixture”) situated nearest to elk and deer. Elk made up most of the diet

followed by white-tailed deer, moose, beaver, and snowshoe hare. My results are

consistent and other estimates using conventional techniques (Urton et al.in prep.,

Meleshko 1986, Carbyn & Kingsley 1979, Carbyn et al. 1985, Paquet 1992). Relative

prey contributions as indicated by IsoSource therefore reflect a realistic picture of

assimilated diet when compared with conventional faecal methods despite each method

representing different time periods.

Consideration of the metabolic pathway between diet and tissue for each element

of interest is important when using stable isotopes to derive consumer diets (Krueger and

Sullivan 1984, Schwarcz 1991, Ambrose and Norr 1993, Tieszen and Fagre 1993). In

carnivores, structural lipids and proteins are assimilated into consumer tissues; however,

carbohydrates (e.g. in plants and berries) consumed by carnivores may not be readily

detected since they are poor sources of nitrogen and carbon tends to be evolved as CO2

during metabolism (Schwarcz 1991, Tieszen and Fagre 1993, Hobson and Stirling 1997).

If wolves consumed plant materials directly, these would add to isotopic variability

(typically by lowering δ15N values) and might not readily be reflected in wolf δ13C

values. Plant materials such as grass and conifer needles have been noted in wolf scat,

however, these materials do not break down during digestion and so their components

would not be assimilated into tissues. On occasion, wolves consume berries for a short

period during the year (Messier, personal communication) but these sparse foraging bouts

likely do not contribute to variability over the period of hair growth.

I have shown intrapopulation variation in stable isotope values previously

unrecognized in wolves. Although isotopic variation is not necessarily synonymous with

70

dietary variation, we consider this the most parsimonious explanation for our boreal study

area. Because the integration of prey resources into hair is over months, the variation I

observed is likely a result of dietary differences and not basal trophic level shifts because

these should average out during the integration period (Bearhop et al. 2004). I was able

to compare directly individuals along a single diversity scale over a known temporal

integration period fulfilling many important assumptions regarding the measurement of

species niche width (Bearhop et al. 2004). Stable isotopic mixing models that focus on

mean isotopic signatures have proved to be a robust measure of diet at the population

level. The possibility of incorporating individual variance in isotope signatures into

dietary mixing models is an important prospect for refining investigations of food web

models, predator-prey interactions, and competition (DeAngelis and Gross 1992).

Moreover, variance in the measurements of proxies that determine niche width among

individual species is ecologically meaningful. Future studies should attempt to refine the

understanding of physiological and environmental factors influencing dietary variation.

Further empirical analysis of intrapopulation niche variation and individual dietary

variance will greatly improve our knowledge of the evolution of complex ecological

interactions involving wolves and other species.

71

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isotopes: the importance of sea ice. Oecologia 133: 70-77. Roth, J. D. and K. A. Hobson. 2000. Stable carbon and nitrogen isotopic fractionation between diet and tissue of captive red fox: implications for dietary reconstruction. Canadian Journal of Zoology 78: 848-852. Schell, D. M., S. M. Saupe, and N. Haubenstock. 1989. Natural isotope abundances in

bowhead whale (Balaena mysticetus) baleen: markers of aging and habitat usage. Pages 261-269 in P.W. Rundel, J.R Ehleringer,a nd K.A. Nagy. Stable Isotopes in Ecological Research. Springer-verlag, New York.

Schell, D. M. and P.J. Ziemann. 1989. Natural Carbon Isotope tracers in Arctic aquatic

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Schwarz, H. P. 1991. Some theoretical aspects of isotope paleodiet studies. Journal of

Archaeological Science 18: 261-275. Scott, B. M. V. and D. M. Shackleton. 1980. Food habits of two Vancouver Island wolf

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of Osteoarchaeology 13: 80-87. Szepanski, M. M., M. Ben-David, and V. Van Ballenberghe. 1999. Assessment of

anadromous salmon resources in the diet of the Alexander Archipelago wolf using stable isotope analysis. Oecologia 120: 327-335.

Terborgh, J., J. Estes, P. Paquet, K. Ralls, D. Boyd-Heger, B. Miller, and R. Noss. 1999. The role of top carnivores in regulating terrestrial ecosystems. Wild Earth 9:

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42-56. Theberge, J. B., S. M. Oosenburg and D. H. Pimlott. 1978. Site and seasonal variation in

food of wolves, Algonquin Park, Ontario. Canadian Field Naturalist 92: 91-94. Tieszen, L. L., J. W. Boutton, K. G. Tesdahl, and N. A. Slade. 1983. Fractionation and turnover of stable carbon isotopes in animal tissues: implications for δ13C analysis of diet. Oecologia 57:32-37. Tieszen, L. L., T. Fagre. 1993. Effects of diet quality and composition on the isotopic

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Vanderklift, M. A. and S. Ponsard. 2003. Sources of variation in consumer-diet δ15N

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wolves, Canis lupus, on wolverines, Gulo gulo, and American marten, Martes americana, in Alaska. Canadian Field Naturalist 116: 132-134.

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NJ, USA.

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5.0 GENERAL DISCUSSION

5.1 Synthesis

The overarching goal of this thesis is to asses how human-caused ecosystem

change affects the behaviour and ecology of wolves. Ultimately, the capacity for animal

populations to adjust to, or withstand rapidly changing environments is unknown.

Although landscape changes are ubiquitous over time, the present rate and scale of

impacts affecting ecosystems are unprecedented. The rapidity and magnitude of such

change raise great concern regarding the ability of animals like wolves to adapt and

survive.

In Europe, widespread environmental changes resulting primarily from

anthropogenic activities resulted in wolf extirpation from all but the most remote regions

of the continent (Zimen and Boitani 1975, Boitani 1992, Randi et al. 1993, 1995, 2000).

However, as small remnant populations now rebuild themselves, they are showing a

remarkable resilience to human-caused disturbances (i.e., Italian wolves: Boitani 1992).

The natural recolonization of wolves to areas in the United States and Canada where they

were also extirpated (Mech 1995, Mech and Boitani 2003) is further evidence of

resilience (Weaver et al. 1996). However, nearly all recovered populations remain at risk

and require direct intervention by managers to ensure survival (Mech and Boitani 2003).

Moreover, the long-term effects of population declines and range contraction, from an

evolutionary perspective, are unknown (Boitani 1992). In theory, decreases in

heterozygosity, because of population bottlenecks and varying degrees of inbreeding,

contribute to decreased survival and reproduction and lower individual and population

fitness (Bouzat et al. 1998, Wayne and Vila 2003, Leonard et al. 2004). Clarifying

whether genetic diversity and structure of a population have a natural basis or result from

human activities and fragmentation of range is important (Wayne and Vila 2003).

Human-caused environmental change, if not slowed, presents complicated

challenges for wildlife management. Carnivore populations across North America have

demonstrated susceptibility to human-caused fragmentation of the landscape (Proctor

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2003, Wayne et al 1992). Wolverines, for example, have increased genetic population

structure at the southern edge of their North American distribution (Kyle and Strobeck

2002). Lynx (Campbell 2002) and grizzly bear (Proctor 2003) populations were found to

be genetically structured across a major highway. Fragmentation may limit the ability for

dispersal to affect “source to sink” processes (Proctor 2003). Fragmentation may also

increase selective pressure on wolves by increasing human-caused mortality near the

perimeters of populations. National parks are a primary example of this phenomenon

(Newmark 1995, Woodruffe and Ginsberg 1998, Callaghan 2002).

Pack instability created by human-caused wolf mortalities, as I propose in Chapter

2, raises the possibility that, although much of the area surrounding PANP is contiguous

with the park, potential habitat may not provide security due to human activity (e.g.

hunting, trapping, roads) outside the park. Most North American wolf populations have

been managed without consideration of harvest times, characteristics of areas where

harvest is allowed, or methods used to kill wolves. In addition, few jurisdictions have

monitored the effects of harvesting (Hayes and Gunson 1995).

Approximately 4-11% of wolves are trapped annually in North America (varying

regionally) (Boitani 2003). Although rigorous assessments are lacking, this rate of

mortality is not believed to limit wolf populations, except along the southern fringes of

their distribution (Theberge 1991, Hayes and Gunson 1995, Boitani 2003). Wolf

populations in my study area are at the southern limit of their range where habitat

fragmentation and human activity are prominent.

My data represent a 1-year snapshot of the genetic structure and diversity of wolf

populations in central Saskatchewan. Wolf populations are considered plastic given wolf

vagility, and if sampled over multiple years, the NW population may show more

admixture in subsequent years relative to 2003. The same population should be sampled

over time in order to identify possible changes (increases or decreases) in structure.

A fundamental component of wolf habitat is the distribution and abundance of

prey. Specifically, wolves and their prey are inextricably linked. Therefore, diet

composition of wolves provides pertinent information about what prey are important for

sustaining wolf populations. In addition, the wolf’s diet forms the core of human conflict

with this large carnivore (Boitani 2003). Elucidating wolf diets is important because as

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environmental conditions change, relationships between wolves and prey also change. In

Chapter 3, I show that wolves used a wide variety of prey, indicative of a generalist

predator. I show that prey selection is in concordance with optimal foraging theory

where wolves balance risk and energy demands with profitability. Selection of elk as a

prey preferred by wolves may contribute to understanding which habitats are important

for maintaining populations of wolves and which are prime dispersal destinations. If

natal habitat dispersal does occur in wolves, knowledge of prey use is of great benefit

when attempting to understand this dynamic (Sacks et al. 2004).

In Chapter 4, stable isotope techniques provide a robust measure of broad diet,

similar to that of faecal analysis, but with additional information about individual

variation in diet. My results indicate that individual specialization may be more common

in social carnivores than the literature suggests. Variance among individual species is

ecologically meaningful and future studies should attempt to tease apart physiological

and environmental factors influencing this variation. Information about individual diet

variation has profound implications for the ecology of individuals, a facet of ecology

currently neglected. Although examining average dietary patterns is useful for whole

populations, natural selection operates at the individual level. Variation in individual

foraging strategies and habitat preferences affects what we see at the population level and

remains key when studying the evolutionary ecology of organisms. Further, community

relationships elucidated by my stable isotope analyses provided a snap shot of the energy

dynamics among mammals in the boreal forest, lending insight into wolf ecology not

previously documented or expected.

5.2 Conservation and management implications

Conserving genetic diversity is important because of the long-term evolutionary

potential it provides. Rare alleles, or combinations of alleles, may confer no immediate

advantage but could be well suited to future environmental conditions. Small populations

that have lost rare alleles have less potential to adapt (Bouzat et al. 1998, Randi et al.

2000). Thus, prudent biologists and managers should aim at maintaining gene flow

among local populations to alleviate the potential deleterious effects of small populations

and genetic isolation. Accordingly, effective management of wolf populations requires

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some idea of genetic population structure. It is also useful to assess dietary patterns for

wolves within and outside protected areas to enhance our knowledge of predator-prey

relationships that may influence wolf movements, ranges, and subsequent population

structure.

My research provides critical baseline data and insight into the potential

vulnerability of wolf populations that could result from genetic deterioration in national

parks. In addition, it provides context for concerns over genetic viability in other

fragmented wolf populations. This investigation will also significantly contribute to the

development of novel and effective research methods that reduce risk imposed by

invasive research. Once refined, these techniques will have broad applicability for low-

impact investigation of animal populations.

5.3 Conclusion

I have elucidated interesting and potentially important patterns in wolf population

ecology. Although my study was not experimental, I was able to measure variation in an

existing ecological state in a purposeful and organized manner. I was also able to

describe some probable proximate causes for that state.

Exploratory studies of this nature are practical, informative, and serve to stimulate

and direct further research. Wolves are extremely difficult to study at any scale. Capture

and handling are dangerous and stressful for study animals and handlers. Although radio

telemetry has provided ecologists with insights into the ecological plasticity and

variability of many species, non-invasive genetic and isotopic techniques have recently

enabled new insights in wildlife research.

Additional research should be considered for wolf populations in central

Saskatchewan. Aside from further investigation into wolf population genetics and

foraging ecology, fine-scale habitat modeling may be required to explain wolf

distribution. Whether wolves are uniformly distributed across the landscape or if

dispersal by wolves is density dependent is unknown. Furthermore, very little is known

about the density, distribution, and abundance of prey in this area. The findings of this

study highlight the need for continued research in the central boreal ecosystem of

Saskatchewan.

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5.4 References

Boitani, L. 1992. Wolf research and conservation in Italy. Biological Conservation 61: 125-132.

Boitani, L. 2003. Wolf conservation and recovery. Pages 317-343 in L.D. Mech and L.

Boitani, editors. Wolves: behavior, ecology, and conservation. University of Chicago Press, Chicago, USA. 448 pp.

Bolnick, D. I., R. Svanback, J. A. Fordyce, L. H.Yang, J. M. Davis, C. D. Hulsey, and M. L. Forister. 2003. The ecology of individuals: incidence and implications of individual specialization. American Naturalist 161: 1-28. Bouzat, J. L., H. H. Cheng, H. A. Lewin, R. L. Westemeier, J. D. Brawn and K. N Paige.

1998. Genetic evaluation of a demographic bottleneck in the Greater prairie chicken. Conservation Biology 12: 836-843.

Callaghan, C. J. 2002. The ecology of Gray wolf (Canis lupus) habitat use, survival, and persistence in the central Rocky mountains, Canada. Ph. D. Thesis. University of Guelph, ON, Canada. Campbell, V. A. 2002. Population genetic structure of Canada lynx (Lynx canadensis) in Alberta. M.Sc. Thesis. University of Alberta, Edmonton, AB. 74pp. Hayes, R. D. and J. R. Gunson. 1995. Status and management of wolves in Canada.

Pages 21-33 in L. N. Carbyn, S. H. Fritts, and D. R. Siep, editors. Ecology and Conservation of wolves in a changing world. Canadian Circumpolar Institute, Edmonton, AB.

Kyle, C. J. and C. Strobeck. 2002. Connectivity of peripheral and core populations of North American wolverines. Journal of Mammalogy 83: 1141-1150. Mech, L. D. 1995. The challenge and opportunity of recovering wolf populations.

Conservation Biology 9: 270-278. Mech, L. D. and L. Boitani. 2003. Wolf social ecology. Pages 317-343 in L.D.Mech and

L. Boitani, editors. Wolves: behavior, ecology, and conservation. University of Chicago Press, Chicago. 448 pp.

Newmark, W. D. 1995. A land-bridge perspective on mammalian extinctions in western North American national parks. Conservation Biology 9: 512-526. Proctor, M. F. 2003. Genetic analysis of movement, dispersal, and population fragmentation of grizzly bears in southwestern Canada. Ph.D. Thesis. University of Calgary, Calgary, AB. 147 pp.

83

Randi, E., F. Francisci, and V. Lucchini. 1995. Mitochondrial DNA restriction

fragment-length polymorphism in the Italian wolf (Canis lupus). Journal of Zoology, Systematics, Evolution and Resources 3:97-100.

Randi, E., V. Lucchini, M. F. Christensen, N. Mucci, S. M. Funk, G. Dolf, and V.

Loeschcke. 2000. Mitochondrial DNA variability in Italian and East European wolves: detecting the consequences of small population size and hybridization. Conservation Biology 14: 464-473.

Randi, E., V. Lucchini, and F. Francisci. 1993. Allozyme variability in the Italian wolf (Canis lupus) population. Heredity 71: 516-522. Taberlet, P., L. P. Waits and G. Luikart. 1999. Noninvasive genetic sampling: look

before you leap. Trends in Ecology and Evolution 14: 323-327. Theberge, J. B. 1991. Ecological classification, status, and management of the gray wolf (Canis lupus) in Canada. Canadian Field Naturalist 83:122-128. Wayne, R. K. and C. Vila. 2003. Molecular genetic studies of wolves. Pages 317-343 in

L.D. Mech and L. Boitani, editors. Wolves: behavior, ecology, and conservation. University of Chicago Press, Chicago. 448 pp.

Wayne, R.K., N. Lehman, M.W. Allard, R. L. Honeycutt. 1992. Mitochondrial DNA variability of the gray wolf – genetic consequences of population decline and habitat fragmentation. Conservation Biology 6: 559-569. Woodroffe, R and J. R. Ginsberg. 1998. Edge effects and the extinction of populations inside protected areas. Science 280: 2126-2128. Zimen, E., and L. Boitani. 1975. Number and distribution of wolves in Italy. Z. Saugetierk. 40:102-112.

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APPENDIX A.

Caveats in non-invasive genetic sampling

Faecal material yields low quantities of degraded DNA (Taberlet et al. 1996,

Luccini et al. 2002). Target DNA extracted from non-invasive samples might produce

contamination, allelic dropout, or scoring of false alleles (Taberlet et al. 1996, Luccini et

al. 2002, Miller et al. 2002). Therefore, any large-scale project should be carefully

assessed through pilot studies.

It is important to address the limitations associated with conducting non-invasive

genetic sampling and analyses. When measuring genetic diversity in a population, allelic

dropout (only 1 allele of a heterozygous individual is detected) can reduce the observed

heterozygosity (HO), potentially resulting in erroneous concerns about extinction risks

caused by inbreeding, low Ne, or low genetic variation (Taberlet et al. 1999). When

investigating genetic population structure (Fst and genetic distance), erroneous estimates

of allele frequencies might alter Fst estimates, Nm, and phylogenies. Reduced Ho can

also generate a false Wahlund effect wrongly suggesting that substructure exists within

the sample population (Taberlet et al. 1999). However, in these cases the severity of

these errors is low. An instance where one must be overly cautious in interpreting results

is when using these data for paternity and parentage analyses or population assignments.

(Taberlet et al. 1999)

Some major technical challenges may be alleviated by (Taberlet et al. 1999): 1)

limiting the degradation of DNA before extraction by maximizing sample preservation,

2) using tissue specific extraction kits that have greatly improved extraction success over

classical phenol-chloroform DNA extraction, 3) using “hot start” PCR amplificaiton

allowing for more PCR cylces that detect single target molecules when conditions are

optimized, 4) using a “multiple tube approach” where repeat PCR experiments with

multiple aliquots of the same DNA extract are performed; this method is useful for

detecting errors, and 5) avoiding contamination by conducting pre- and post- PCR

85

experiments in separate labs, minimizing handling of PCR extracts, and using negative

controls.

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References

Luccini, V., E. Fabbri, F. Marucco, S. Ricci, L. Boitani, and E. Randi. 2002. Noninvasive molecular tracking of colonizing wolf (Canis lupus) packs in the Western Italian Alps. Molecular Ecology 11: 857-868. Miller, C., P. Joyce, L. P. Waits. 2002. Assessing allelic dropout and genotype reliability using maximum likelihood. Genetics 160: 357-366. Taberlet, P., S. Griffin, B. Goosens, et al. 1996. Reliable genotyping of samples with very low DNA quantities using PCR. Nucleic Acids Research 24: 3189-3194. Taberlet, P., L. P. Waits, and G. Luikart. 1999. Noninvasive genetic sampling: look

before you leap. Trends in Ecology and evolution 14: 323-327.

87